Journal articles: 'Computer crop modeling'

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Kobayashi, Kent D. "COMPUTER SIMULATION PROGRAMS FOR TEACHING CROP MODELING." HortScience 27, no. 6 (June 1992): 671e—671. http://dx.doi.org/10.21273/hortsci.27.6.671e.

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The simulation programs Stella® (High Performance Systems) and Extend™ (Imagine That!) were used on Apple® Macintosh® computers in a graduate course on crop modeling to develop crop simulation models. Students developed models as part of their homework and laboratory assignments and their semester project Stella offered the advantage of building models using a relational diagram displaying state, rate, driving, and auxiliary variables. Arrows connecting the variables showed the relationships among the variables as information or material flows. Stella automatically kept track of differential equations and integration. No complicated programming was required of the students. Extend used the idea of blocks representing the different parts of a system. Lines connected the inputs and outputs to and from the different blocks. Extend was more flexible than Stella by giving the students the opportunity to do their own programming in a language similar to C. Also, with its dialog boxes, Extend more easily allowed the students to run multiple simulations answering “What if” questions. Both programs quickly enabled students to develop crop simulation models without the hindrance of extensive learning of a programming language or delving deeply into the mathematics of modeling.

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Heisler, M. G., and H. Jönsson. "Computer Modeling of Plant Development." Journal of Plant Growth Regulation 25, no. 4 (November 24, 2006): 267–69. http://dx.doi.org/10.1007/s00344-006-0080-z.

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Lin, Yuke, and Ying Zhang. "Research on Precision Cultivation of Digital Multimedia Crop Based on Predictive Computational Intelligence Technology." Advances in Multimedia 2021 (December 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/2864009.

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With the intersection and integration of modern crop cultivation and emerging disciplines, crop cultivation management is moving from traditional modeling and standardization to quantitative and intelligent direction. Crop precision cultivation technology is to apply system science and information technology to crop cultivation and digitally design information perception of the objects and processes involved in crop cultivation, dynamic simulation, so as to realize the quantification and accuracy of crop cultivation management. With the integration and intersection of modern digital multimedia crop cultivation and emerging disciplines, digital multimedia crop cultivation tends to gradually implement the quantitative and intelligent development, replacing the traditional scale and standardization. The technology of digital multimedia crop cultivation is to use science and information technology in digital multimedia crops to achieve the quantitative and precise characteristics of digital multimedia crop cultivation. The advancement of digital multimedia crop cultivation technology has greatly improved the management and benefits of the entire agricultural industry and has played a positive role in the development of agricultural information and modernity.

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Abdulhameed, Isam M., Sonay Sozudogru OK, Hala N. Malloki, Muhittin Onur Akca, Bilge Omar, and Gokhan Cayci. "Bio-Saline Agriculture Modeling, Using Saline Water for Irrigation Purposes." International Journal of Design & Nature and Ecodynamics 17, no. 6 (December 31, 2022): 951–56. http://dx.doi.org/10.18280/ijdne.170617.

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Climate change effects increase the scarcity of irrigation water and deterioration of its quality, which affects the crop water requirements. Researchers were studying the water recycling technique and finding about other possible renewable water resources for irrigation, they conclude that saline water can be used to meet part of the irrigation water needs for many crops under special field management, because there are many crops have a high tolerance to the salinity without decreasing in the yield. The current study aims to evaluate the economic yield of saline drainage water in irrigation. A computer program (Fıuat Ujaj) using Visual Basic language was constructed to use the largest possible amount of drainage water for irrigation after removing the toxic effects and then calculates the relative yield of the selected crops. The Main Outflow Drain (MOD) in Iraq was selected as a saline water resource which has 4.63 dS m-1 Electrical Conductivity (EC). This saline water contained high concentrations of chlorine and sodium ions. MFUP results showed that toxic effect of these ions can be removed by diluting with 35% of the nearby river water. MFUP results showed that the crops with high and medium tolerance to salinity give an acceptable yield ratio when they were irrigated with diluted water (35%) to remove toxicity effects only, while the dilution increases for crops of medium sensitivity, but the acceptable yield of sensitive crops is not achieved except with fresh river water. If the crop production is lower than the economically acceptable limit, another 5% of the river water will be added to mitigate, and the dilution process continues until the percentage of the river water become 80% of the irrigation water. If the required product is not achieved, then the program instructs to irrigate this crop with the river water only.

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Wang, Ze Fang, and Chen Liu. "Study on Greenhouse System Modeling Based on Adaptive Fuzzy Predictive Control." Applied Mechanics and Materials 602-605 (August 2014): 1237–39. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1237.

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In recent years, these governments are committed to intelligent greenhouse research, intelligent greenhouse system is a kind of resource conservation effective agricultural development technology, it is in the common greenhouse basis, combined with modern computer automatic control technology, intelligent sensing technology, artificial intelligence and expert system in high-tech field to develop, provide seasonal irrelevant for crop growth environment in a computer integrated control, to realize the various crops industrial production of high quality、 high efficient and low consumption[1]. With computer as the core of greenhouse comprehensive environment control system , get rapid development in Europe and the United States and Japan, then entered the network intelligent stage.Study of domestic greenhouse control system started relatively late, to the 80's, have the microcomputer control of artificial climate chamber , such as the Chongqing research institute MCU control system of the artificial climate chamber, as well as Shanghai the plant research institute artificial climate chamber[2].

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Ma, Xu, Tiejun Wang, and Lei Lu. "A Refined Four-Stream Radiative Transfer Model for Row-Planted Crops." Remote Sensing 12, no. 8 (April 18, 2020): 1290. http://dx.doi.org/10.3390/rs12081290.

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In modeling the canopy reflectance of row-planted crops, neglecting horizontal radiative transfer may lead to an inaccurate representation of vegetation energy balance and further cause uncertainty in the simulation of canopy reflectance at larger viewing zenith angles. To reduce this systematic deviation, here we refined the four-stream radiative transfer equations by considering horizontal radiation through the lateral “walls”, considered the radiative transfer between rows, then proposed a modified four-stream (MFS) radiative transfer model using single and multiple scattering. We validated the MFS model using both computer simulations and in situ measurements, and found that the MFS model can be used to simulate crop canopy reflectance at different growth stages with an accuracy comparable to the computer simulations (RMSE < 0.002 in the red band, RMSE < 0.019 in NIR band). Moreover, the MFS model can be successfully used to simulate the reflectance of continuous (RMSE = 0.012) and row crop canopies (RMSE < 0.023), and therefore addressed the large viewing zenith angle problems in the previous row model based on four-stream radiative transfer equations. Our results demonstrate that horizontal radiation is an important factor that needs to be considered in modeling the canopy reflectance of row-planted crops. Hence, the refined four-stream radiative transfer model is applicable to the real world.

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Kyryzyuk, Sergii, Vitaliy Krupin, Olena Borodina, and Adam Wąs. "Crop Residue Removal: Assessment of Future Bioenergy Generation Potential and Agro-Environmental Limitations Based on a Case Study of Ukraine." Energies 13, no. 20 (October 14, 2020): 5343. http://dx.doi.org/10.3390/en13205343.

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This study assesses the bioenergy generation potential of crop residues in Ukraine for the year 2030. Projections of agricultural development are made based on the Global Biosphere Management Model (GLOBIOM) and verified against available Agricultural Member State Modeling (AGMEMOD) results in regard to the six main crops cultivated in Ukraine (wheat, barley, corn, sunflower, rape and soya). Two agricultural development scenarios are assessed (traditional and innovative), facilitating the projection of future crop production volumes and yields for the selected crops. To improve precision in defining agro-environmental limitations (the share of crop residues necessary to be kept on the fields to maintain soil fertility for the continuous cultivation of crops), yield-dependent residue-to-product ratios (RPRs) were applied and the levels of available soil nutrients for regions of Ukraine (in regard to nitrogen, phosphorus, potassium and humus) were estimated. The results reveal the economically feasible future bioenergy generation potential of crop residues in Ukraine, equaling 3.6 Mtoe in the traditional agricultural development scenario and 10.7 Mtoe in the innovative development scenario. The projections show that, within the latter scenario, wheat, corn and barley combined are expected to provide up to 81.3% of the bioenergy generation potential of crop residues.

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Ma, Xu, and Yong Liu. "A Modified Geometrical Optical Model of Row Crops Considering Multiple Scattering Frame." Remote Sensing 12, no. 21 (November 2, 2020): 3600. http://dx.doi.org/10.3390/rs12213600.

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The canopy reflectance model is the physical basis of remote sensing inversion. In canopy reflectance modeling, the geometric optical (GO) approach is the most commonly used. However, it ignores the description of a multiple-scattering contribution, which causes an underestimation of the reflectance. Although researchers have tried to add a multiple-scattering contribution to the GO approach for forest modeling, different from forests, row crops have unique geometric characteristics. Therefore, the modeling approach originally applied to forests cannot be directly applied to row crops. In this study, we introduced the adding method and mathematical solution of integral radiative transfer equation into row modeling, and on the basis of improving the overlapping relationship of the gap probabilities involved in the single-scattering contribution, we derived multiple-scattering equations suitable for the GO approach. Based on these modifications, we established a row model that can accurately describe the single-scattering and multiple-scattering contributions in row crops. We validated the row model using computer simulations and in situ measurements and found that it can be used to simulate crop canopy reflectance at different growth stages. Moreover, the row model can be successfully used to simulate the distribution of reflectances (RMSEs < 0.0404). During computer validation, the row model also maintained high accuracy (RMSEs < 0.0062). Our results demonstrate that considering multiple scattering in GO-approach-based modeling can successfully address the underestimation of reflectance in the row crops.

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Bekele, Bewket Getachew. "Review on Yield Gap Analysis: Modeling of Achievable Yields at Farm Level." European Journal of Agriculture and Forestry Research 10, no. 2 (February 15, 2022): 21–27. http://dx.doi.org/10.37745/ejafr.2013/vol9n22127.

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In the present context, ‘model’ is expressed as a computer program that can be repeatedly run several times for computing several designed mathematical or statistical expressions (equations) governing crop growth-environment relations, given appropriate input data. The experiment station yields obtained under a rainfed situation without any nutrient deficiency mostly considered as the potential yields of rainfed crops. Actual yields are obtained by recording crop yields of farmers in the region under investigation and achievable yield is between actual and potential yield. Actual yields are compared with the potential yields to estimate yield gaps of crops for that area and others which have the same agro-ecology. Achievable yield is determined by factors like availability of moisture and nutrients, Precipitation and irrigation as input, Soil profile water holding characteristics, Plant water balance (transpiration, water uptake), Soil water balance (evaporation, infiltration, runoff, flow, drainage) and Nitrogen fertilizer applications as input, Soil nitrogen conditions, Plant nitrogen balance (uptake, fixation, mobilization), Soil nitrogen balance (mineralization, immobilization, nitrification, denitrification). Generally, modeling Achievable yield of farm depend on water and nutrient data of the area and Actual yield is determined by factors like weeds, insect pests, diseases and pollutants.

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Pierre Pott, Luan, Telmo Jorge Carneiro Amado, Raí Augusto Schwalbert, Geomar Mateus Corassa, and Ignacio Antonio Ciampitti. "Crop type classification in Southern Brazil: Integrating remote sensing, crop modeling and machine learning." Computers and Electronics in Agriculture 201 (October 2022): 107320. http://dx.doi.org/10.1016/j.compag.2022.107320.

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Bischokov, Ruslan M. "Fuzzy logic device for crop analysing, modeling and forecasting in the Kabardino-Balkarian Republic." RUDN Journal of Agronomy and Animal Industries 15, no. 2 (December 15, 2020): 123–33. http://dx.doi.org/10.22363/2312-797x-2020-15-2-123-133.

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Using computer fuzzy-logical models based on empirical values of climatic characteristics (rainfall, temperature and humidity) of long-term observations (1955-2018) from meteorological stations in the Kabardino-Balkarian Republic (Nalchik, Baksan, Prokhladny and Terek) and crop yields (winter wheat, spring wheat, corn, sunflower, millet, oats), dependence of crop yields on variations of climatic factors were analyzed and a specific forecast was given. Setting expected values of climatic characteristics in computer model, we received possible values of productivity for the next season. Uniformity assessment (Dixon and Smirnov - Grabbsas criterion), stability (Student and Fischers criterion), statistical importance of parameters of distribution and accidental errors were determined. Originality of the method is in the fact that in the form of input parameters of the model predictors, the previously calculated forecast values of the meteorological parameters for the next agricultural year were used, and at the output, the predicted values of crop productivity were obtained as predictants. Furthermore, recommendations on adoption of management decisions were developed.

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Badenko, Vladimir, Vitaly Terleev, and Alexander Topaj. "AGROTOOL Software as an Intellectual Core of Decision Support Systems in Computer Aided Agriculture." Applied Mechanics and Materials 635-637 (September 2014): 1688–91. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1688.

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Some problems of decision support systems in computer aided agriculture are discussed. The main focus is made on collaborative model development, including model decomposition issues and implementation of generic frameworks for polyvariant model use. A current state and prospective ideas for improvement of modeling infrastructure suitable to perform multi-factor computer experiments with existing crop simulation models are presented.

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Russell, Mark H., and Raymond J. Layton. "Models and Modeling in a Regulatory Setting: Considerations, Applications, and Problems." Weed Technology 6, no. 3 (September 1992): 673–76. http://dx.doi.org/10.1017/s0890037x00036034.

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Computer based simulation models are increasingly being used to predict the environmental fate of crop protection chemicals. Some considerations that need to be given in selecting appropriate models for regulatory purposes include model applicability, validation, capability, ease of use, and documentation. Problems commonly encountered in modeling include limited accuracy, lack of defined objectives and standard modeling practices, and misuse of models and results. Models will continue to play an important role in the regulation of crop protection chemicals. It is important that regulators and industry agree on appropriate models and practices, and that regulatory decisions are not based solely on model results but take into account all available data.

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Zhang, Fengli, Chen Li, Yajie Yu, and Dana M. Johnson. "Resources and Future Availability of Agricultural Biomass for Energy Use in Beijing." Energies 12, no. 10 (May 14, 2019): 1828. http://dx.doi.org/10.3390/en12101828.

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The increasing importance of lignocellulosic biomass based energy production has led to an urgent need to conduct a reliable resource supply assessment. This study analyses and estimates the availability of agricultural residue biomass in Beijing, where biomass energy resources are relatively rich and is mainly distributed in the suburbs. The major types of crops considered across Beijing include food crops (e.g., maize, winter wheat, soybean, tubers and rice), cotton crops and oil-bearing crops (e.g., peanuts). The estimates of crop yields are based on historical data between 1996 and 2017 collected from the Beijing Municipal Bureau of Statistics. The theoretical and collectable amount of agricultural residues was calculated on the basis of the agricultural production for each crop, multiplied by specific parameters collected from the literature. The assessment of current and near future agricultural residues from crop harvesting and processing resources in Beijing was performed by employing three advanced modeling methods: the Time Series Analysis Autoregressive moving average (ARMA) model, Least Squares Linear Regression and Gray System Gray Model (GM) (1,1). The results show that the time series model prediction is suitable for short-term prediction evaluation; the least squares fitting result is more accurate but the factors affecting agricultural waste production need to be considered; the gray system prediction is suitable for trend prediction but the prediction accuracy is low.

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Sunitha, Gurram, M. N. Pushpalatha, A. Parkavi, Prasanthi Boyapati, Ranjan Walia, Rachna Kohar, and Kashif Qureshi. "Modeling of Chaotic Political Optimizer for Crop Yield Prediction." Intelligent Automation & Soft Computing 34, no. 1 (2022): 423–37. http://dx.doi.org/10.32604/iasc.2022.024757.

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Díaz-González, Vicente, Alejandro Rojas-Palma, and Marcos Carrasco-Benavides. "How Does Irrigation Affect Crop Growth? A Mathematical Modeling Approach." Mathematics 10, no. 1 (January 4, 2022): 151. http://dx.doi.org/10.3390/math10010151.

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This article presents a qualitative mathematical model to simulate the relationship between supplied water and plant growth. A novel aspect of the construction of this phenomenological model is the consideration of a structure of three phases: (1) The soil water availability, (2) the available water inside the plant for its growth, and (3) the plant size or amount of dry matter. From these phases and their interactions, a model based on a three-dimensional nonlinear dynamic system was proposed. The results obtained showed the existence of a single equilibrium point, global and exponentially stable. Additionally, considering the framework of the perturbation theory, this model was perturbed by incorporating irrigation to the available soil water, obtaining some stability results under different assumptions. Later through the control theory, it was demonstrated that the proposed system was controllable. Finally, a numerical simulation of the proposed model was carried out, to depict the soil water content and plant growth dynamic and its agreement with the results of the mathematical analysis. In addition, a specific calibration for field data from an experiment with wheat was considered, and these parameters were then used to test the proposed model, obtaining an error of about 6% in the soil water content estimation.

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Al-Adhaileh, Mosleh Hmoud, and Theyazn H. H. Aldhyani. "Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia." PeerJ Computer Science 8 (September 30, 2022): e1104. http://dx.doi.org/10.7717/peerj-cs.1104.

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Predicting crop yields is a critical issue in agricultural production optimization and intensification research. Accurate foresights of natural circumstances a year in advance can have a considerable impact on management decisions regarding crop selection, rotational location in crop rotations, agrotechnical methods employed, and long-term land use planning. One of the most important aspects of precision farming is sustainability. The novelty of this study is to evidence the effective of the temperature, pesticides, and rainfall environment parameters in the influence sustainable agriculture and economic efficiency at the farm level in Saudi Arabia. Furthermore, predicting the future values of main crop yield in Saudi Arabia. The use of artificial intelligence (AI) to estimate the impact of environment factors and agrotechnical parameters on agricultural crop yields and to anticipate yields is examined in this study. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model was built to accurately predict the crop yield, temperature, insecticides, and rainfall based on environmental data. The dataset is collected from different Saudi Arabia regions from 1994 to 2016, including the temperature, insecticides, rainfall, and crop yields for potatoes, rice, sorghum, and wheat. For this study, we relied on five different statistical evaluation metrics: the mean square error (MSE), the root-mean-square error (RMSE), normalized root mean square error (NRMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Analyses of datasets for crop yields, temperature, and insecticides led to the development of the MLP models. The datasets are randomly divided into separate samples, 70% for training and 30% for testing. The best-performing MLP model is characterized by values of (R = 100%) and (R2 = 96.33) for predicting insecticides in the testing process. The temperature, insecticides, and rainfall were examined with different crop yields to confirm the effectiveness of these parameters for increasing product crop yields in Saudi Arabia; we found that these items had highest relationships. The average values are R = 98.20%, 96.50, and 99.14% with for the temperature, insecticides, and rainfall, respectively. Based on these findings, it appeared that each of the parameter categories that are considered (temperature, pesticides, and rainfall) had a similar contribution to the accuracy of anticipated yield projection.

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Lasso, Emmanuel, Natacha Motisi, Jacques Avelino, and Juan Carlos Corrales. "FramePests: A Comprehensive Framework for Crop Pests Modeling and Forecasting." IEEE Access 9 (2021): 115579–98. http://dx.doi.org/10.1109/access.2021.3104537.

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Barbosa, Alexandre, Rodrigo Trevisan, Naira Hovakimyan, and Nicolas F. Martin. "Modeling yield response to crop management using convolutional neural networks." Computers and Electronics in Agriculture 170 (March 2020): 105197. http://dx.doi.org/10.1016/j.compag.2019.105197.

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Baurley, James W., Arif Budiarto, Muhamad Fitra Kacamarga, and Bens Pardamean. "A Web Portal for Rice Crop Improvements." International Journal of Web Portals 10, no. 2 (July 2018): 15–31. http://dx.doi.org/10.4018/ijwp.2018070102.

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High quality models of factors influencing rice crop yield are needed in countries where rice is a staple food. These models can help select optimal rice varieties for expected field conditions. Development of a system to help scientist track and make decisions using this data is challenging. It involves incorporation of complex data structures - genomic, phenotypic, and remote sensing - with computationally intensive statistical modeling. In this article, the authors present a web portal designed to help researchers to manage and analyze their datasets, apply machine learning to detect how factors taken together influence crop production, and summarize the results to help scientists make decisions based on the learned models. The authors developed the system to be easily accessed by the entire team including rice scientist, genetics, and farmers. As such, they developed a system on a server architecture comprised of a SQLite database, a web interface developed in Python, the Celery job scheduler, and statistical computing in R.

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Borodina, O. M., S. V. Kyryziuk, O. V. Fraier, Y. M. Ermoliev, T. Y. Ermolieva, P. S. Knopov, and V. M. Horbachuk. "Mathematical Modeling of Agricultural Crop Diversification in Ukraine: Scientific Approaches and Empirical Results*." Cybernetics and Systems Analysis 56, no. 2 (March 2020): 213–22. http://dx.doi.org/10.1007/s10559-020-00237-6.

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Rokochinskiy, A. M., V. O. Turcheniuk, P. P. Volk, R. M. Koptyuk, N. V. Prykhodko, and D. M. Rychko. "Water needs of interplanted crops on rice irrigation systems." Міжвідомчий тематичний науковий збірник "Меліорація і водне господарство", no. 1 (June 25, 2020): 102–11. http://dx.doi.org/10.31073/mivg202001-232.

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Relevance of research. Recent studies of weather and climatic conditions of the rice-growing zone of Ukraine indicate a steady tendency to increase the aridity of the climate in the region. Further increase in air temperature and decrease in natural water availability of these territories will lead to the increase in total evaporation and water needs for irrigation of the crops of rice crop rotation. Under such conditions a significant exacerbation of the existing problem of water deficit is expected in the region. The availability of water resources directly affects the efficiency of agricultural production on the irrigated lands of rice systems. In this regard, there is an objective need to adapt agricultural production on the irrigated lands of rice systems to the existed and predicted climate change, which, first of all, requires the assessment of water needs for irrigation both the leading crop of flooded rice and the interplanted crops of rice crop rotation. Aim of the study is to estimate the changes in water needs for irrigation of the interplanted crops of rice crop rotation in the variable natural-agro-reclamation conditions of rice system functioning. To achieve this goal, the authors implemented a large-scale computer experiment, based on a complex of predictive-simulation models, which basing on a long-term forecast, allow to estimate weather and climatic conditions, water regime, water regulation technologies and the productivity of reclaimed lands. During the experiment the conditions of total evaporation formation were investigated, the water needs of different types of interplanted crops of rice crop rotation were determined for the technology and regime of water regulation on the irrigated lands of rice systems for the typical groups of vegetation periods of target years in view of general heat and moisture provision. It was evaluated technological efficiency of irrigation of the interplanted crops of rice crop rotation in the variable natural-agro-reclamation conditions of rice system functioning and obtained results with the actual production data were compared. Research methods. The research methods were based on the application of system theory along with the systematic approach, system analysis and modeling oriented on widespread use of computers and related software in the developing of modern approaches to substantiate of technical and technological solutions for water regulation on the drained lands in the conditions of climate change. The object of the study is the Danube rice irrigation systems in Odessa region, design, natural and reclamation conditions of which are typical for the most of rice systems in Ukraine. Results of the study and the main conclusions. During the computer experiment the conditions of total evaporation formation were investigated, the water needs of different types of interplanted crops of rice crop rotation were determined for the technology and regime of water regulation on the irrigated lands of rice systems for the typical groups of vegetation periods of target years in view of general heat and moisture provision. Technological efficiency of irrigation of the interplanted crops of rice crop rotation in the variable natural-agro-reclamation conditions of rice system functioning was evaluated and the obtained results with the actual production data were compared. This approach makes it possible to evaluate and predict water needs for irrigation of the interplanted crops of rice crop rotation in the variable natural-agro-reclamation conditions of rice system functioning. Prospects. The obtained results can be effectively used for justification of regime and technological decisions in the projects of reconstruction and modernization of existing rice systems and developing adaptive measures to the predicted climate change in the region.

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Bandaru, Varaprasad, Raghu Yaramasu, Curtis Jones, R. César Izaurralde, Ashwan Reddy, Fernando Sedano, Craig S. T. Daughtry, Inbal Becker-Reshef, and Chris Justice. "Geo-CropSim: A Geo-spatial crop simulation modeling framework for regional scale crop yield and water use assessment." ISPRS Journal of Photogrammetry and Remote Sensing 183 (January 2022): 34–53. http://dx.doi.org/10.1016/j.isprsjprs.2021.10.024.

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Saravi, Babak, A. Pouyan Nejadhashemi, Prakash Jha, and Bo Tang. "Reducing deep learning network structure through variable reduction methods in crop modeling." Artificial Intelligence in Agriculture 5 (2021): 196–207. http://dx.doi.org/10.1016/j.aiia.2021.09.001.

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LAKTIONOV, IVAN, OLEKSANDR VOVNA, and VLADYSLAV BORYCHEVSKYI. "RESULTS OF DEVELOPMENT AND TESTING OF COMPUTER-INTEGRATED TECHNOLOGY OF GREENHOUSES ARTIFICIAL LIGHTING CONTROL." Herald of Khmelnytskyi National University 303, no. 6 (December 2021): 201–6. http://dx.doi.org/10.31891/2307-5732-2021-303-6-201-206.

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The development and implementation of the automation and digitalization technologies of growing crops production processes in industrial greenhouses is being actualized for increasing of crop yields. This fact, in turn, positively influences on the re-equipment of the software and hardware base of protected soil domestic agricultural production, which stimulates an increase of investment attractiveness and long-term sustainability of Ukrainian agricultural enterprises. The main purpose of the article is developing of the scientific approaches on creation and testing of computer-integrated technology for artificial lighting control in the protected ground agricultural production conditions. The research object is automatic control process of artificial lighting. The research subject is methods and hardware-software components of the indoor greenhouse microclimate. Research methods are analysis of existing development methods, mathematical and computer modeling, hardware and software experimental testing. In course of the research, the component base has been substantiated and the block diagram of the hardware and software for the control of lighting technology has been developed. As a result, a computer model has been synthesized and tested, which is implemented on microprocessor technology and algorithms of the fuzzy logic theory for control the intensity and spectral composition of LED lamps in greenhouses. The prototype of control system for artificial additional lighting of greenhouse crops has been implemented and experimentally investigated. The implemented hardware and software means of computer-integrated technology allow to automatically control the parameters of LED lamps, taking into account the types and periods of growing crops.

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Goldsmith, Peter D., and Jean-Christophe Dissart. "Computer-based scenario modeling: Application to swine industry." Agribusiness 14, no. 4 (July 1998): 281–98. http://dx.doi.org/10.1002/(sici)1520-6297(199807/08)14:4<281::aid-agr3>3.0.co;2-o.

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DeJong, T. M., R. Favreau, Y. L. Grossman, and G. Lopez. "USING CONCEPT-BASED COMPUTER SIMULATION MODELING TO STUDY AND DEVELOP AN INTEGRATED UNDERSTANDING OF TREE CROP PHYSIOLOGY." Acta Horticulturae, no. 903 (August 2011): 751–57. http://dx.doi.org/10.17660/actahortic.2011.903.104.

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Li, Qingzhen, and A. N. Leonov. "Modeling and optimization of technological process and means of mechanization of grain production for middle China based on Belarusian technology." Proceedings of the National Academy of Sciences of Belarus. Agrarian Series 58, no. 1 (February 10, 2020): 90–107. http://dx.doi.org/10.29235/1817-7204-2020-58-1-90-107.

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One of the main problems in grain production in China is the high level of unit costs (high cost price). The main reason is the low level of mechanization. Development of efficient technologies and means of mechanization is a promising area allowing to decrease the level of unit costs at a given labor efficiency and maintaining the achieved yield. Grain production is a complex system associated with a large amount of information: agro-technological (crop variety, crop yield, physical-and-mechanical parameters of land plots, terms of operations, permissible speed range for specific operations, etc.), technical-and-economic (power and traction parameters, throughput, working width, operating weight, hopper volume , cost). At present, efficient methods for studying complex systems have appeared as a result of development and widespread implementation of computer mathematics systems, which allow us to study mechanization technologies and tools using multi-factor modeling and multi-criteria optimization. The paper presents a multi-factor mathematical model, peculiar for the fact that the three groups of simultaneously varying factors are taken for the first time engine power, MTA speed, timing of the main energy-intensive operations – plowing, harvesting, and as conflicting optimization parameters unit cost level, coefficient of crop losses, labor efficiency, which allowed a comprehensive study of the grain production process in any natural-production conditions. A technological process has been developed (units speed and timing of the main operations (plowing, harvesting) and the corresponding range of machines and equipment, ensuring minimum level of unit costs at a given labor efficiency and maintaining the achieved crop yield, taking into account the specifics of the Middle China (2 crops per year).

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Ibragimova, R. R., G. F. Yartsev, R. K. Baikasenov, T. P. Aysuvakova, B. B. Kartabayeva, V. I. Tseiko, and V. M. Kosolapov. "Productivity And Quality Of Spring Soft Wheat Grain Depending On Root Feeding With Liquid Nitrogen Fertilizers On Black Soils Of South Orenburg Region." IOP Conference Series: Earth and Environmental Science 901, no. 1 (November 1, 2021): 012061. http://dx.doi.org/10.1088/1755-1315/901/1/012061.

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Abstract The relevance of the topic of scientific research is associated with the use of new liquid fertilizers and a growth regulator in spring wheat crops to increase the yield and quality of grain in the central zone of the Orenburg region. Ensuring food security today is becoming one of the most urgent tasks set by the government of the Russian Federation for domestic agriculture. To solve this problem, it is necessary, first of all, to increase the yield of agricultural crops, rationally using all the factors affecting it. One of these factors is the timely and sufficient application of mineral fertilizers, the cost of which largely determines the size of the cost of production. The emergence of precision farming is associated, first of all, with the improvement of all types of agricultural machinery and technologies, as well as with the rapid development of computer technology, methods of computer modeling and information technology. The integrating basis of the technology is geographic information systems that allow registering and processing information characterizing the state of soil and crops. This information makes it possible to effectively use one of the most significant resources in crop production - mineral fertilizers.

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Ghosh, Lopa, and G. N. Tiwari. "Computer Modeling of Dissolved Oxygen Performance in Greenhouse Fishpond: An Experimental Validation." International Journal of Agricultural Research 3, no. 2 (February 15, 2008): 83–97. http://dx.doi.org/10.3923/ijar.2008.83.97.

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Shnaidman, V. M., and R. Sh Zhemukhov. "Environmental Aspects in Mathematical Modeling of Irrigation Systems Planning." Water Science and Technology 26, no. 5-6 (September 1, 1992): 1439–47. http://dx.doi.org/10.2166/wst.1992.0587.

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This paper deals with applying computer-oriented technology while selecting water and land resources management strategies lor irrigation systems with special reference to environmental aspects. The technology is based on a system of coordinated mathematical models. The system includes a simulation model of irrigation system functioning, a model for irrigation water demand computation, a set of simplified mathematical models of the anthropogenic impact, viz. soil salinization, non-point pollutions from agricultural plots, rising level of subsurface water and its mineralization. The system also takes into account equations for crop yields as functions of both water consumption and fertilization. This makes it possible to analyze various strategies of irrigation system management with the help of a multicriterial procedure. The models are described in sufficient detail and a computation example is given.

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Williams, J. R., C. W. Richardson, and R. H. Griggs. "The Weather Factor: Incorporating Weather Variance Into Computer Simulation." Weed Technology 6, no. 3 (September 1992): 731–35. http://dx.doi.org/10.1017/s0890037x00036125.

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The effect of weather variation on pesticide losses was estimated with the Erosion-Productivity Impact Calculator (EPIC) model. Weather variations had little effect on pesticide loss from a hypothetical site near Memphis, TN, but the effect was more dramatic and in the expected direction at Des Moines, IA. Atrazine losses at Des Moines were reduced by lowering relative humidity or rainfall intensity. Increasing the CO2 level from 300 to 660 ppm slightly increased atrazine losses. Results from these two sites are very limited and only serve to demonstrate modeling potential for addressing weather/pesticide problems. Further, more comprehensive studies are needed to better estimate pesticide loss sensitivity to weather variation.

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Drahniev, S. V., and T. A. Zheliezna. "Energy balance estimation of growing corn for grain with alienation of its by-products for bioenergy needs." Mehanization and electrification of agricultural, no. 13(112) (2021): 187–95. http://dx.doi.org/10.37204/0131-2189-2021-13-21.

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Annotation Purpose. Energy assessment of growing corn for grain with a comparison of two options for the use of corn by-products: partial collection of the by-products as solid biofuel for heat production or leaving all the crop residues in the field with further plowing them into the soil and heating with natural gas. Methods. Theoretical methods of designing technological processes in crop production, energy assessment of mechanized crop production technologies, life cycle analysis and computer modeling. Results. A comprehensive energy analysis with the inclusion of all technological processes of growing corn for grain, partial harvesting of its by-products and further use for heat production compared to plowing all the crop residues into the soil and using natural gas for heating. Conclusions. It is established that by meeting the agronomic requirements for the alienation of corn for energy needs, significant savings in anthropogenic energy per unit of heat produced compared to the option of plowing all the crop residues into the soil and using natural gas for heating. Keywords: energy analysis, corn, by-products, crop residues, biofuel, heat, heating.

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Borges Júnior, João C. F., Paulo A. Ferreira, Camilo L. T. Andrade, and Bettina Hedden-Dunkhorst. "Computational modeling for irrigated agriculture planning. Part I: general description and linear programming." Engenharia Agrícola 28, no. 3 (September 2008): 471–82. http://dx.doi.org/10.1590/s0100-69162008000300008.

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Linear programming models are effective tools to support initial or periodic planning of agricultural enterprises, requiring, however, technical coefficients that can be determined using computer simulation models. This paper, presented in two parts, deals with the development, application and tests of a methodology and of a computational modeling tool to support planning of irrigated agriculture activities. Part I aimed at the development and application, including sensitivity analysis, of a multiyear linear programming model to optimize the financial return and water use, at farm level for Jaíba irrigation scheme, Minas Gerais State, Brazil, using data on crop irrigation requirement and yield, obtained from previous simulation with MCID model. The linear programming model outputted a crop pattern to which a maximum total net present value of R$ 372,723.00 for the four years period, was obtained. Constraints on monthly water availability, labor, land and production were critical in the optimal solution. In relation to the water use optimization, it was verified that an expressive reductions on the irrigation requirements may be achieved by small reductions on the maximum total net present value.

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Taghvaeian, Saleh, Allan A. Andales, L. Niel Allen, Isaya Kisekka, Susan A. O’Shaughnessy, Dana O. Porter, Ruixiu Sui, Suat Irmak, Allan Fulton, and Jonathan Aguilar. "Irrigation Scheduling for Agriculture in the United States: The Progress Made and the Path Forward." Transactions of the ASABE 63, no. 5 (2020): 1603–18. http://dx.doi.org/10.13031/trans.14110.

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HighlightsThe progress made in agricultural irrigation scheduling in the past ten years and the current challenges are discussed.The main scientific scheduling strategies are based on soil water status, plant characteristics, and crop modeling.Challenges include large time and data requirements and availability of decision support systems.Opportunities include integration of scheduling strategies and demonstrating their effectiveness through local studies.Abstract. Irrigation scheduling is the process of determining the appropriate amount and timing of water application to achieve desired crop yield and quality, maximize water conservation, and minimize possible negative effects on the environment, such as nutrient leaching below the crop root zone. Effective irrigation scheduling has been shown to save water, save energy, and help agricultural producers achieve improved yields and quality. However, scientific irrigation scheduling methods generally have remained limited to mostly research applications with relatively low adoption by irrigators. There are several main approaches to irrigation scheduling, including those based on soil water status, plant characteristics, and/or crop modeling. Each of these approaches has advantages as well as limitations and sources of uncertainty and variability, depending on application conditions. This article summarizes progress made in the U.S. in each of the main scheduling approaches in the past ten years (since the 2010 Decennial Irrigation Symposium) and existing challenges and opportunities that should be considered moving forward. This article is intended to guide future research and extension projects in improving adoption of scientific irrigation scheduling approaches. Keywords: Computer modeling, Plant characteristics, Soil water status.

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Aghelpour, Pouya, Hadigheh Bahrami-Pichaghchi, and Farzaneh Karimpour. "Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH." Complexity 2022 (July 12, 2022): 1–18. http://dx.doi.org/10.1155/2022/4534822.

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Evapotranspiration represents the water requirement of plants during their growing season, and its accurate measurement at the farm is essential for agricultural water planners and managers. Field measurements of evapotranspiration have always been associated with many difficulties that have led researchers to seek a way to remotely measure this component in horticultural and agricultural areas. This study aims to investigate an indirect approach for daily rice crop evapotranspiration (ETc) measurement by machine learning (ML) techniques and the least available climatic variables. For this purpose, daily meteorological variables were obtained from three ground meteorological stations in rice cultivation regions of northern Iran during 2003–2016. The ETc rates were calculated by seven meteorological variables, the FAO-56 Penman-Monteith equation, and the regional calibrated rice crop coefficient and considered as the reference data. The MLs, including Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Group Method of Data Handling (GMDH), were utilized for ETc modeling. Different input combinations were applied, based on the use of minimum effective variables as input. Results showed that the models showed the most accurate performances in the input combination of four climatic variables: sunshine duration, maximum temperature, relative humidity, and wind speed. Investigating the accuracy of models in rice growth phases showed that the least estimation error belonged to the initial growing stage, which increased during the mid-season and late-season growing stages. A comparison of the models showed that the GMDH model performed better against the other competitors. For this model, both the Nash-Sutcliffe (NS) coefficient and R2 were greater than 0.98, and the Root Mean Square Error (RMSE) ranged between 0.214 and 0.234 mm per day in all stations. The current approach showed promising results in rice evapotranspiration modeling by only four common meteorological variables and can be reliably applied for indirect measurement of this variable over the rice farms. The studied approach will have research value for rice and other crops in similar/different climatic conditions.

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Bai, X. D., Z. G. Cao, Y. Wang, Z. H. Yu, X. F. Zhang, and C. N. Li. "Crop segmentation from images by morphology modeling in the CIE L*a*b* color space." Computers and Electronics in Agriculture 99 (November 2013): 21–34. http://dx.doi.org/10.1016/j.compag.2013.08.022.

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Gostev, A. V., A. I. Pykhtin, and N. I. Lyubitsky. "Software of Rational Choice of Adaptive Technologies for the Cultivation of Grain Crops as an Element of Agriculture Digitalization." Proceedings of the Southwest State University 23, no. 6 (February 23, 2020): 189–209. http://dx.doi.org/10.21869/2223-1560-2019-23-6-189-209.

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Purpose of research. The goal of research is to develop a science-based decision support system for agricultural producers to choose adaptive technologies for growing crops in the European part of Russia.Methods. System approach, database design theory, mathematical modeling, software and information systems development theory, software qualimetry methods.Results. The paper consistently addresses the issues of the need to digitalize agriculture, describes the results of current research on this topic, identifies areas for further research of such developments, and describes the process of creating and testing application software in stages on the example of our own research. As a result of the conducted research, a finished product has been created and tested. It is a computer program that solves not only the problem of increasing the profitability of grain production, but also ensures the environmental orientation of the technologies used, which is extremely important and relevant at the present time. The proposed software package consists of a client-server application for personal computers, a web application, a mobile application for smartphones based on the Android operating system, and two databases (for personal computers and for the online version of the application).Conclusion. There has been created software that allows us to select the technology for cultivating the given grain crop, taking into account the prevailing soil and climatic conditions of a particular territory, and, thus,it can help to increase the profitability of grain production, ensure the environmental orientation of the applied technologies by effectively using mineral fertilizers, fuel and chemical plant protection productsand select the optimal variety or hybrid of grain crops. It can also help to choose agricultural machinery, taking into account the requirements of import substitution and preliminary calculation of the economic efficiency of the proposed agricultural technology.

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Kiniry, J. R., J. R. Williams, D. J. Major, R. C. Izaurralde, P. W. Gassman, M. Morrison, R. Bergentine, and R. P. Zentner. "EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region." Canadian Journal of Plant Science 75, no. 3 (July 1, 1995): 679–88. http://dx.doi.org/10.4141/cjps95-114.

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The EPIC computer simulation model has potential for assessing agricultural management scenarios in the northern Great Plains region of the United States and western Canada. The objectives of this study were to develop parameters for economically important crop and forage species grown in these regions and to determine whether EPIC could use these parameters to reasonably simulate yields. Parameters for leaf-area development, temperature responses, biomass growth and partitioning, and nutrient concentrations were derived from data in the literature for spring canola, wheat, barley, maize and six forage species. Because of the growing importance of canola in Canada and the United States, much emphasis was placed on deriving its parameters. With these inputs, EPIC reasonably simulated forage and crop yields in six locations and canola yields in four locations. The model should provide reasonable simulations for a wide range of applications throughout these regions. Key words: simulation modeling, canola, agricultural management

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Novak, Hrvoje, Marko Ratković, Mateo Cahun, and Vinko Lešić. "An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development." Telecom 3, no. 1 (January 6, 2022): 70–85. http://dx.doi.org/10.3390/telecom3010004.

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Actual and upcoming climate changes will evidently have the largest impact on agriculture crop cultivation in terms of reduced harvest, increased costs, and necessary deviations from traditional farming. The aggravating factor for the successful applications of precision and predictive agriculture is the lack of granulated historical data due to slow, year-round cycles of crops, as a prerequisite for further analysis and modeling. A methodology of plant growth observation with the rapid performance of experiments is presented in this paper. The proposed system enables the collection of data with respect to various climate conditions, which are artificially created and permuted in the encapsulated design, suitable for further correlation with plant development identifiers. The design is equipped with a large number of sensors and connected to the central database in a computer cloud, which enables the interconnection and coordination of multiple geographically distributed devices and related experiments in a remote, autonomous, and real-time manner. Over 40 sensors and up to 24 yearly harvests per device enable the yearly collection of approximately 750,000 correlated database entries, which it is possible to independently stack with higher numbers of devices. Such accumulated data is exploited to develop mathematical models of wheat in different growth stages by applying the concepts of artificial intelligence and utilizing them for the prediction of crop development and harvest.

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Kitzler, Florian, Helmut Wagentristl, Reinhard W. Neugschwandtner, Andreas Gronauer, and Viktoria Motsch. "Influence of Selected Modeling Parameters on Plant Segmentation Quality Using Decision Tree Classifiers." Agriculture 12, no. 9 (September 6, 2022): 1408. http://dx.doi.org/10.3390/agriculture12091408.

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Modern precision agriculture applications increasingly rely on stable computer vision outputs. An important computer vision task is to discriminate between soil and plant pixels, which is called plant segmentation. For this task, supervised learning techniques, such as decision tree classifiers (DTC), support vector machines (SVM), or artificial neural networks (ANN) are increasing in popularity. The selection of training data is of utmost importance in these approaches as it influences the quality of the resulting models. We investigated the influence of three modeling parameters, namely proportion of plant pixels (plant cover), criteria on what pixel to choose (pixel selection), and number/type of features (input features) on the segmentation quality using DTCs. Our findings show that plant cover and, to a minor degree, input features have a significant impact on segmentation quality. We can state that the overperformance of multi-feature input decision tree classifiers over threshold-based color index methods can be explained to a high degree by the more balanced training data. Single-feature input decision tree classifiers can compete with state-of-the-art models when the same training data are provided. This study is the first step in a systematic analysis of influence parameters of such plant segmentation models.

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Shaffer, M. J., B. J. Newton, and C. M. Gross. "An Internet-Based Simulation Model for Nitrogen Management in Agricultural Settings." Scientific World JOURNAL 1 (2001): 728–36. http://dx.doi.org/10.1100/tsw.2001.337.

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Complex chemical, physical, and biological processes mediate nitrogen (N) transformations and movement during agricultural production, making the optimization of fertilizer use and environmental protection exceedingly difficult. Various computer models have been developed to simulate the site-specific fate and transport of N resulting from different crop production scenarios, but these models are very complex and difficult to use for most farmers, consultants, and conservationists. In an effort to facilitate access and simplify the use of sophisticated models, the U.S. Department of Agriculture (USDA) has developed an Internet-based nitrogen analysis tool. Based on the Nitrate Leaching and Economic Analysis Package (NLEAP), the Web site allows a user to conduct multiyear N simulation modeling specific to a crop field. Servers handle much of the required data assembly and formatting, thus sparing the user�s resources. Model runs are executed on the servers and the results are transmitted to the user. This new tool is presented along with early implementation results.

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Borges Júnior, João C. F., Paulo A. Ferreira, Camilo L. T. Andrade, and Bettina Hedden-Dunkhorst. "Computational modeling for irrigated agriculture planning. Part II: risk analysis." Engenharia Agrícola 28, no. 3 (September 2008): 483–93. http://dx.doi.org/10.1590/s0100-69162008000300009.

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Techniques of evaluation of risks coming from inherent uncertainties to the agricultural activity should accompany planning studies. The risk analysis should be carried out by risk simulation using techniques as the Monte Carlo method. This study was carried out to develop a computer program so-called P-RISCO for the application of risky simulations on linear programming models, to apply to a case study, as well to test the results comparatively to the @RISK program. In the risk analysis it was observed that the average of the output variable total net present value, U, was considerably lower than the maximum U value obtained from the linear programming model. It was also verified that the enterprise will be front to expressive risk of shortage of water in the month of April, what doesn't happen for the cropping pattern obtained by the minimization of the irrigation requirement in the months of April in the four years. The scenario analysis indicated that the sale price of the passion fruit crop exercises expressive influence on the financial performance of the enterprise. In the comparative analysis it was verified the equivalence of P-RISCO and @RISK programs in the execution of the risk simulation for the considered scenario.

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Park, Donghyeok, Chun Gu Lee, Doee Yang, Daehyun Kim, Joon Yong Kim, and Joong Yong Rhee. "Analysis of Inter-particle Contact Parameters of Garlic Cloves Using Discrete Element Method." Journal of Biosystems Engineering 46, no. 4 (October 18, 2021): 332–45. http://dx.doi.org/10.1007/s42853-021-00110-0.

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Abstract Purpose The discrete element method (DEM) can be used in agricultural fields such as crop sowing, harvesting, and crop transportation. Nevertheless, modeling complex crops as appropriately shaped particles remains challenging. The modeling of particles and the calibration of input parameters are important for simulating the realistic behaviors of particles using the DEM. Methods In this study, particle models representing the morphological characteristics and size deviations of garlic cloves were proposed. Additionally, the coefficients of friction were analyzed as the contact parameters of the particles based on the heap formation experiments and simultations of the swing-arm method using 150 garlic cloves. Results The simulation results were analyzed that the residual number of particles, a bulk property that can be measured simply in the experiment, is related to the coefficients of friction. In the heap formation experiments with low particle counts, the bulk properties were more clearly differentiated by the residual number of particles than the angle of repose. Moreover, the bulk properties similar to the actual garlic could not be expressed as a spherical particle model. Thus, an equation for predicting the residual number of particles was derived for the non-spherical garlic clove particle model. Five sets of coefficients of friction were presented using the prediction equation, and all the simulation results were close to the actual residual number of particles and angle of repose of the garlic. Conclusions Although the sizes of garlic cloves have a wide distribution, appropriate inter-particle contact parameters could be predicted. Therefore, the calibration process of the DEM can be shortened using the proposed prediction equation for the residual number of particles with non-spherical particles.

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Lybecker, Donald W., Edward E. Schweizer, and Robert P. King. "Weed Management Decisions in Corn Based on Bioeconomic Modeling." Weed Science 39, no. 1 (March 1991): 124–29. http://dx.doi.org/10.1017/s0043174500057982.

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A fixed (conventional) weed management strategy in corn was compared to three other strategies (two mixed and one flexible) in terms of weed control, grain yield, gross margin (gross income minus herbicide treatment costs), and herbicide use under furrow irrigation for four consecutive years. The fixed strategy prespecified preplanting, preemergence, postemergence, and layby herbicides. The flexible strategy herbicide treatments were specified by a computer bioeconomic model. Model decisions were based on weed seed in soil before planting, weed densities after corn emergence, herbicide costs, expected corn grain yield and selling price, and other parameters. The two mixed strategies were a combination of fixed and flexible strategies and designated either specified soil-applied herbicides (mixed/soil), or no soil-applied herbicide (mixed/no soil); postemergence treatments were determined by the model. Average corn grain yield was 10 280 kg ha–1and gross income was 920 $ ha–1and neither differed among strategies. Total weed density and gross margin were significantly higher for the mixed/no soil and flexible strategies compared to the mixed/soil and fixed strategies. Total weed density averaged 28 720, 28 100, 10 910, and 680 plants ha–1for the mixed/no soil, flexible, mixed/soil, and fixed strategies, respectively. Annual gross margins for the four strategies averaged 885, 875, 845, and 810 $ ha–1, respectively. Herbicide use over the 4-yr period for these four strategies averaged 3.8, 5.3, 20.5, and 26.9 kg ha–1, respectively, and each value differed from the other. Thus, weeds can be managed in corn, gross margins increased, and herbicide use decreased by employing a bioeconomic weed-corn model to make weed management decisions.

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Tang, Dahua, Yu Feng, Daozhi Gong, Weiping Hao, and Ningbo Cui. "Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands." Computers and Electronics in Agriculture 152 (September 2018): 375–84. http://dx.doi.org/10.1016/j.compag.2018.07.029.

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DeJong, Ted M., Romeo Favreau, Mitch Allen, and Przemyslaw Prusinkiewicz. "Modeling Fruit Tree Architectural Growth, Source–Sink Interactions, and Physiology with L-PEACH." HortScience 41, no. 4 (July 2006): 1010D—1010. http://dx.doi.org/10.21273/hortsci.41.4.1010d.

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Modeling source–sink interactions and carbohydrate partitioning in plants requires a detailed model of plant architectural development, in which growth and function of each organ is modeled individually and carbohydrate transport among organs is modeled dynamically. L-PEACH is an L-system-based graphical simulation model that combines supply/demand concepts of carbon partitioning with an L-system model of tree architecture to create a distributed supply/demand system of carbon allocation within a growing tree. The whole plant is modeled as a branching network of sources and sinks, connected by conductive elements. An analogy to an electric network is used to calculate the flow and partitioning of carbohydrates between the individual components. The model can simulate multiple years of tree growth and be used to demonstrate effects of irrigation, crop load, and pruning on architectural development, tree growth, and carbon partitioning. Qualitative model outputs are viewed graphically as the tree “grows” on the computer screen while quantitative output data can be evaluated individually for each organ or collectively for an organ type using the MatLab software.

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Białobrzewski, I. "Neural modeling of relative air humidity." Computers and Electronics in Agriculture 60, no. 1 (January 2008): 1–7. http://dx.doi.org/10.1016/j.compag.2007.02.009.

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Crimaldi, Mariano, Fabrizio Cartenì, and Francesco Giannino. "VISmaF: Synthetic Tree for Immersive Virtual Visualization in Smart Farming. Part I: Scientific Background Review and Model Proposal." Agronomy 11, no. 12 (December 2, 2021): 2458. http://dx.doi.org/10.3390/agronomy11122458.

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Computer-Generated Imagery (CGI) has received increasing interest in both research and the entertainment industry. Recent advancements in computer graphics allowed researchers and companies to create large-scale virtual environments with growing resolution and complexity. Among the different applications, the generation of biological assets is a relevant task that implies challenges due to the extreme complexity associated with natural structures. An example is represented by trees, whose composition made by thousands of leaves, branches, branchlets, and stems with oriented directions is hard to be modeled. Realistic 3D models of trees can be exploited for a wide range of applications including decision-making support, visualization of ecosystem changes over time, and for simple visualization purposes. In this review, we give an overview of the most common approaches used to generate 3D tree models, discussing both methodologies and available commercial software. We focus on strategies for modeling and rendering of plants, highlighting their accordance or not with botanical knowledge and biological models. We also present a proof of concept to link biological models and 3D rendering engines through Ordinary Differential Equations.

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Ekanayake, Piyal, Lasini Wickramasinghe, and Jeevani W. Jayasinghe. "Development of Crop-Weather Models Using Gaussian Process Regression for the Prediction of Paddy Yield in Sri Lanka." International Journal of Intelligent Systems and Applications 14, no. 4 (August 8, 2022): 52–665. http://dx.doi.org/10.5815/ijisa.2022.04.05.

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This research introduces machine learning models using the Gaussian Process Regression (GPR) depicting the association between paddy yield and weather in Sri Lanka. All major regions in the island with most contribution to the total paddy production were considered in this research. The climatic factors of rainfall, relative humidity, minimum temperature, maximum temperature, average wind speed, evaporation, and sunshine hours were considered as input (independent) variables, while the paddy yield was the output (dependent) variable. The collinearity within each pair of independent and dependent variables was determined using Spearman’s and Pearson’s correlation coefficients. Data sets corresponding to the two main annual paddy cultivation seasons since 2009 were trained in MATLAB to develop crop-weather models. The most appropriate Kernel function was chosen from among four types of Kernels viz. Rational Quadratic, Exponential, Squared Exponential, and Matern 5/2 based on their degree of coherence in modeling. This approach exploits the full potential of GPR in developing highly accurate crop-weather models. The performance of the crop-weather models was measured by the Correlation Coefficient, Mean Absolute Percentage Error, Mean Squared Error, Root Mean Squared Error Ratio, Nash Number and the BIAS. All the GPR-based models proposed in this paper are highly accurate in terms of the aforementioned evaluation metrics. Accordingly, when the climatic data are known or projected, the paddy yield and thereby the harvest of Sri Lanka can be predicted precisely by using the proposed crop-weather models.

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Journal articles on the topic 'Computer crop modeling'

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