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Academic literature on the topic 'Precision farming'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Precision farming"

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Bhatia, Kartikeya, and Devendra Duda. "Precision Farming." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 403–6. http://dx.doi.org/10.31142/ijtsrd22793.

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Yasam, Mr Srinath, and Dr S. Anu H. Nair. "ecision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Agriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture Context Agriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture." International Journal of Engineering and Advanced Technology 9, no. 1s5 (December 30, 2019): 74–80. http://dx.doi.org/10.35940/ijeat.a1023.1291s519.

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The scope of sensor networks and the Internet of Things spanning rapidly to diversified domains but not limited to sports, health, and business trading. In recent past, the sensors and MEMS integrated Internet of Things are playing crucial role in diversified farming strategies like dairy farming, animal farming, and agriculture farming. The usage of sensors and IoT technologies in farming are coined in contemporary literature as smart farming or precision farming. At its early stage of smart farming, the practices applying in agriculture farming are limited to collect the data related to the context of farming, such as soil state, weather state, weed state, crop quality, and seed quality. These collections are to help the farmers, scientists to conclude the positive and negative factors of crop to initiate the required agricultural practices. However, the impact of these practices taken by the agriculturists depends on their experience. In this regard, the computer-aided predictive analytics by machine learning and big data strategies are having inevitable scope. The emphasis of this manuscript is reviewing the existing set of computer-aided methods of predictive analytics defined in related to precision farming, gaining insights into how distinct set of precision farming inputs are supporting the predictive analytics to help farming communities towards improvisation. It is imperative from the review of the literature that right from the farming process and techniques to usage of distinct sets of farming precision models like the machine learning solutions and other such factors indicate that there are potential ways in which the precision farming solutions can be resourceful for the farming groups. Optical sensing, soil analysis, imagery processing based analysis, machine learning models that can support in effective prediction are some of the key areas wherein the numbers of solutions that have offered from the market are high. From the compiled sources of literature in the study, there must be many techniques, tools, and available solutions, but one of the key areas wherein the solutions are turning complex for the companies is about usage of the comprehensive kind of machine learning models used in the precision farming which is currently a major gap and is potential scope for the future research process. This contemporary review indicating that both supervised and unsupervised machine learning models are yielding results, still in terms of improvements that are essential in precision farming. The overall efforts of this review portraying that, there is a need for developing a system that can self-train on the critical features based on the loop model of features gathered from the process and make use of such inputs for analysis. If such clustered solution is gathered, it can help in improving the quality of analysis based on the learning practices and the historical data captured from the systems aligned.

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Norton, T., and D. Berckmans. "Developing precision livestock farming tools for precision dairy farming." Animal Frontiers 7, no. 1 (January 1, 2017): 18–23. http://dx.doi.org/10.2527/af.2017.0104.

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Hamrita, Takoi K., Jeffrey S. Durrence, and George Vellidis. "Precision farming practices." IEEE Industry Applications Magazine 15, no. 2 (March 2009): 34–42. http://dx.doi.org/10.1109/mias.2009.931816.

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Tutkun, Muhittin. "PRECISION DAIRY FARMING." Journal of Agricultural, Food and Environmental Sciences 77, no. 1 (2023): 12–19. http://dx.doi.org/10.55302/jafes23771012t.

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Gnip, P., and K. Charvát. "Management of zones in precision farming." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (March 2, 2012): 416–18. http://dx.doi.org/10.17221/5425-agricecon.

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Precision farming is a very fast developing form of the Farm Management System, especially in crop production, in whole world and in our country as well. There, it is adopted since the second half of the 90s of the 20th century. The system of data collection, analysis, presentation and application of information in precision farming is reaching over the possibilities of their use by common farmers or agricultural companies. Service companies in this case play a very important role as an executor of exacting analysis, data collection and their presentation. Management zones present simplification of the difficult operations and recommendations including economic calculations for the common user involved in the precision farming management.

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Győrffy, Béla. "From Organic to Precision Farming (Contemporary Publication)." Acta Agraria Debreceniensis, no. 9 (December 10, 2002): 81–86. http://dx.doi.org/10.34101/actaagrar/9/3565.

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The paper presents a short review of the different types of farming systems:Biofarming, Organic farming, Alternatíve farming, Biodynamic farming, Low input sustainable agriculture (LISA)Mid-tech farming, Sustainable agriculture, Soil conservation farming, No till farming, Environmentally sound, Environmentally friendly, Diversity farmingCrop production system, Integrated pest management (IPM), Integrated farming, High-tech farmingSite specific production (SSP), Site specific technology (SST), Spatial variable technology, Satellite farming.Precision farmingIt concludes that the various systems are applicable in different ratios and combinations depending on the natural and economic conditions.The author predicts an increase in precision technologies , the first step being the construction of yield maps compared with soil maps and their agronomic analysis. Based on this information, it will be necessary to elaborate the variable technology within the field, especially for plant density, fertilization and weed control.The changes in weed flora during the past fifty years based on 10.000 samples within the same fields using the weed cover method are presented.

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Mandal, Manas, Bappa Paramanik, Anamay Sarkar, and Debasis Mahata. "PRECISION FARMING IN FLORICULTURE." International Journal of Research -GRANTHAALAYAH 9, no. 1 (January 26, 2021): 75–77. http://dx.doi.org/10.29121/granthaalayah.v9.i1.2021.2871.

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Precision farming is a science base modern technology which provided management concept based on observation and response to intra-field variations. New technologies such as Global Positioning Systems (GPS), sensors, satellites or aerial images and Geographical Information Systems (GIS) are utilized to assess and analyse variations in agricultural and horticultural production. In this technology have two primary goals that are (i) optimum return (ii) preserving resource. Wireless Sensor Networks has crucial role to management of water resources, to assess the optimum point of harvesting, to estimate fertilizer requirements and to predict crop performance more accurately, disease and pest hazard also. Sensors use to precision farming technology in horticulture, which increasing productivity, decreasing production costs and minimizing the environmental impact of farming. Though precision farming has vital role in Agriculture and Horticulture sector but, no so popular due to high cost of technology and need high speed internet facility.

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István Komlósi. "The precision livestock farming." Acta Agraria Debreceniensis, no. 49 (November 13, 2012): 201–2. http://dx.doi.org/10.34101/actaagrar/49/2525.

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The application of information technology is the response of the livestock farming to the demand of customer, legal and economical expectations. This technology is the socalled precision livestock farming (PLF). The elements of the PLF are: continuous monitoring of inputs, animal behaviour by sensors, an algorithm which converts these signals into a figure, this figure is compared to an optimum then adjustment of the input is followed, if it is necesary.

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Zhuravleva, Larisa Anatolyevna. "Precision farming. Soil scanners." Agrarian Scientific Journal, no. 10 (October 27, 2020): 100–106. http://dx.doi.org/10.28983/asj.y2020i10pp100-106.

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The article provides an analysis of devices, installations and sensors that allow you to capture various characteristics of soils, including in real time. A description of a mobile device for measuring the characteristics of soil and crops, which allows you to visualize the information obtained in real time. High performance is provided by installing sensors that determine the parameters of crops and soil moisture on a mobile trailer device and transmit information to a microcontroller, WiFi module, with Internet access.

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Dissertations / Theses on the topic "Precision farming"

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Rusch, Peter C. "Precision farming in South Africa." Diss., Pretoria : [s.n.], 2001. http://upetd.up.ac.za/thesis/available/etd-01072004-153302.

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Blackmore, Simon. "The role of yield maps in precision farming." Thesis, Cranfield University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269521.

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Shelley, Anthony N. "INCORPORATING MACHINE VISION IN PRECISION DAIRY FARMING TECHNOLOGIES." UKnowledge, 2016. http://uknowledge.uky.edu/ece_etds/86.

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The inclusion of precision dairy farming technologies in dairy operations is an area of increasing research and industry direction. Machine vision based systems are suitable for the dairy environment as they do not inhibit workflow, are capable of continuous operation, and can be fully automated. The research of this dissertation developed and tested 3 machine vision based precision dairy farming technologies tailored to the latest generation of RGB+D cameras. The first system focused on testing various imaging approaches for the potential use of machine vision for automated dairy cow feed intake monitoring. The second system focused on monitoring the gradual change in body condition score (BCS) for 116 cows over a nearly 7 month period. Several proposed automated BCS systems have been previously developed by researchers, but none have monitored the gradual change in BCS for a duration of this magnitude. These gradual changes infer a great deal of beneficial and immediate information on the health condition of every individual cow being monitored. The third system focused on automated dairy cow feature detection using Haar cascade classifiers to detect anatomical features. These features included the tailhead, hips, and rear regions of the cow body. The features chosen were done so in order to aid machine vision applications in determining if and where a cow is present in an image or video frame. Once the cow has been detected, it must then be automatically identified in order to keep the system fully automated, which was also studied in a machine vision based approach in this research as a complimentary aspect to incorporate along with cow detection. Such systems have the potential to catch poor health conditions developing early on, aid in balancing the diet of the individual cow, and help farm management to better facilitate resources, monetary and otherwise, in an appropriate and efficient manner. Several different applications of this research are also discussed along with future directions for research, including the potential for additional automated precision dairy farming technologies, integrating many of these technologies into a unified system, and the use of alternative, potentially more robust machine vision cameras.

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Waine, T. "Non-Invasive soil property measurement for precision farming." Thesis, Cranfield University, 1999. http://dspace.lib.cranfield.ac.uk/handle/1826/11322.

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This work investigates the application of new sensors to enable agronomists and farm managers to make decisions for variable treatment strategies at key crop growth stages. This is needed to improve the efficiency of crop production in the context of precision farming. Two non-invasive sensors were selected for investigation. These were: 1) The MGD-1 ion mobility gas detector made by Environics OY, Finland. 2) The EM38 electromagnetic induction (EMI) sensor made by Geonics Inc., Canada. The gas detector was used to determine residual nitrogen and to measure carbon dioxide gas as a surrogate indicator of soil quality. In the latter, increased microbial carbon dioxide production was expected on soils with high organic matter content. Overall, the results of gas detection were disappointing. The main problems inherent in the system were; lack of control of the gas sampling, insufficient machine resolution and cross contamination. This led to the decision to discontinue the gas detection research. Instead, the application of electromagnetic induction (EMI) to measure soil variation was investigated. There were two principle advances in the research. Firstly the application of EMI to the rapid assessment of soil textural class. Secondly the mapping of available water content in the soil profile. These were achieved through the development of a new calibration procedure based on EMI survey of the sites at field capacity, working with field experiments from five sites over two years. Maps of total available water holding capacity were produced. These were correlated with yield maps from wet and dry seasons and used to explain some of the seasonal influences on the spatial variation in yield. A product development strategy for a new EMI sensor was considered which produced a recommendation to design a new EMI sensor specifically for available water content and soil texture mapping, that could be mounted on a tractor. For the first time, this procedure enables routine monitoring of the spatial variation in available water content. This enables the effects of seasonal and spatial variation to be included in crop models, targeted irrigation and to aid decisions for the variable application of inputs.

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Waine, Toby William. "Non-invasive soil property measurement for precision farming." Thesis, Cranfield University, 1999. http://dspace.lib.cranfield.ac.uk/handle/1826/11322.

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This work investigates the application of new sensors to enable agronomists and farm managers to make decisions for variable treatment strategies at key crop growth stages. This is needed to improve the efficiency of crop production in the context of precision farming. Two non-invasive sensors were selected for investigation. These were: 1) The MGD-1 ion mobility gas detector made by Environics OY, Finland. 2) The EM38 electromagnetic induction (EMI) sensor made by Geonics Inc., Canada. The gas detector was used to determine residual nitrogen and to measure carbon dioxide gas as a surrogate indicator of soil quality. In the latter, increased microbial carbon dioxide production was expected on soils with high organic matter content. Overall, the results of gas detection were disappointing. The main problems inherent in the system were; lack of control of the gas sampling, insufficient machine resolution and cross contamination. This led to the decision to discontinue the gas detection research. Instead, the application of electromagnetic induction (EMI) to measure soil variation was investigated. There were two principle advances in the research. Firstly the application of EMI to the rapid assessment of soil textural class. Secondly the mapping of available water content in the soil profile. These were achieved through the development of a new calibration procedure based on EMI survey of the sites at field capacity, working with field experiments from five sites over two years. Maps of total available water holding capacity were produced. These were correlated with yield maps from wet and dry seasons and used to explain some of the seasonal influences on the spatial variation in yield. A product development strategy for a new EMI sensor was considered which produced a recommendation to design a new EMI sensor specifically for available water content and soil texture mapping, that could be mounted on a tractor. For the first time, this procedure enables routine monitoring of the spatial variation in available water content. This enables the effects of seasonal and spatial variation to be included in crop models, targeted irrigation and to aid decisions for the variable application of inputs.

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Eastwood, Callum Ross. "Innovatoive precision dairry systems : a case study of farmer learning and technology co-development /." Connect to thesis, 2008. http://repository.unimelb.edu.au/10187/3530.

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Yang, Chun-Chieh. "Development of a weed management system for precision farming." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0033/NQ64697.pdf.

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Yang, Chun-Chieh 1967. "Development of a weed management system for precision farming." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36735.

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The primary objective of this research project is to develop a system for precision spraying of herbicides in a corn field. Ultimately, such a system would permit real-time image collection, processing, weed identification, mapping of weed density and sprayer control using a tractor-mounted digital camera and on-board computer. The initial hypotheses underlying this project were (1) that it is possible to train an artificial neural network (ANN) to distinguish weeds from a crop species (corn in this study); (2) that it is possible to differentiate between weed species; and (3) that precision spraying can significantly reduce the quantity of herbicide needed to protect crop yields, thus reducing both the costs and environmental impacts of such applications. Thus, development of an ANN for this purpose was the main focus of the research project.
Since the success of ANN development is primarily dependent on the type of information that it is provided, much of the work involved investigation of different approaches to extracting information from the digital images of field sections and individual objects (weeds or corn plants), as well as analysis of the type of information extracted. The applicability of a given image processing method was evaluated in terms of the image recognition accuracy, as well as the computer time and memory requirements for processing and obtaining ANN output, since speed is of the essence in real-time applications. The greenness method based on a pixel-by-pixel analysis of red-green-blue intensity value of the original images was the most successful and was used in further work.
As it turned out, ANN development for this purpose was difficult. While the success rate for recognition of corn plants was high (80% or greater), the success rate for recognition of weeds tended to be low. Improvements in weed recognition were met with decreases in the success rate of corn recognition. Differentiation between weed species was less than desirable. Differentiation between corn and a given weed species was also not as good, particularly when the weed species was similar in appearance to the young corn plant.
Therefore, another strategy was developed to recognize weeds in the field by taking images between the corn rows. Previously, the images were taken randomly in the field. The images were processed to obtain percent greenness in each image and this information was used to create weed coverage and weed patchiness maps. Based on these maps, herbicide spraying was decided and spraying amounts were determined. In terms of real-time, it was possible to process the equivalent of one metre of row per second. Although this is slow compared to tractor speed in the field, the computer was not operating under dedicated conditions as one would require for the real-time application. Thus, the results were considered encouraging.
The final stage of the work involved an evaluation of the potential herbicide savings from a precision spraying system. This was done by using the weed coverage and weed patchiness maps as inputs to a simulated fuzzy logic controller, and integrating the output of the controller over the field area corresponding to the input images. The simulations with different fuzzy rules and membership functions indicated that the precision spraying approach could reduce the amount of herbicide needed for weed control in a corn field by up to 15%.

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Schneider, Martin [Verfasser]. "Ökonomische Potenziale von Precision Farming unter Risikoaspekten / Martin Schneider." Aachen : Shaker, 2011. http://d-nb.info/106904833X/34.

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Cillis, Donato. "Introducing innovative precision farming techniques in agriculture to decrease carbon emissions." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3425242.

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Nowadays, agricultural systems are asked to satisfy the increasing global demand for food and fiber for a growing population. The intensification of the current systems in term of inputs and outputs lead to raising the concerns about the impact on the environment. Considering the background found in literature and its highlighted gap, the hypothesis of this thesis are (1) to survey within-farm soil and yield variability in order to delineate the homogeneous zones and productive potential; (2) study the synergy between conservation agriculture and precision agriculture allowing the optimization in terms of crop yield, net energy and energy efficiency; (3) identify the best strategies, derived from the synergy between conservation agriculture and precision agriculture, able to decrease the CO2 emissions of agricultural systems in the mid-term using SALUS simulations. Data collection required to verify these hypotheses derived from different sources, and their analysis was performed using different approaches and tools. In fact, different soil and crop sensors were used to define site-specific crop management and enable processes to better understand land changes such as spatial variations or delineation of homogeneous zones at farm scale. Besides, simulation models, when suitably tested, provide a useful tool for finding the combination of management strategies to reach the multiple goals required for sustainable crop production. Simulation models also allow to increase inputs efficiency and to perform land management. The homogeneous zones characterization derived from the interpolation of ARP data and historical crop-yield data. Incorporating this method, it is possible to efficiently perform the analysis with a larger set of data. Classification and definition of the homogeneous zones were fulfilled by inputting data into the MZA software. The optimum number of homogeneous classes was identified according to the study of indices provided by the software. Four homogeneous zones satisfying these requirements were then defined. Consequently, the productive potential was assigned to the homogeneous zones through ANOVA test of soil features and historical crop yield. Finally, the productive potential was validated comparing the average province yield of the considered crops. Regarding crop yield, strip-tillage (ST) and no-tillage (NT) got a decrease of 20% and 15% compared to conventional tillage (CT). However, the contribution of precision agriculture allows mitigating crop yield reduction in every tested conservation tillage system. In fact, an increase in total crop yield higher than 10% was observed, leading minimum tillage (MT) to obtain the same response of CT. In the same way, MT supported by precision agriculture achieves the highest net energy values, 2% higher than CT. While precision agriculture enables to enhance of almost 20% net energy in ST and NT compared to the same techniques managed in uniform rate application. Moreover, precision agriculture contributes to increasing energy efficiency in MT and NT with an increase with respect to CT of 10% and 2% respectively. Finally, ST supported by precision agriculture technologies shows an increase in energy efficiency of 15% compared to ST managed with a fixed rate of inputs. On the other hand, the contribution of precision agriculture in term of carbon emissions mitigation was assessed in order to define the strategy with the lowest total annual CO2 emissions under current climatic condition. SALUS simulation shown a general trend among the treatments characterized by a decrease in soil organic carbon (SOC) stock. However, a significant reduction in SOC losses was simulated in MT and NT, 17% and 63% respectively, compared to CT. Furthermore, the adoption of conservation tillage techniques decreased carbon emissions related to farming operations, while precision agriculture technologies led to an optimization of the exhaustible sources such as fossil fuels and fertilizers. Finally, we showed that the synergy between conservation tillage systems, especially NT, and precision agriculture strategies represents a useful tool in terms of carbon emissions mitigation. With consideration of current climatic conditions and the studied field variability, NT supported by precision agriculture strategies demonstrated a reduction of 56% of total CO2 as compared to CT.
Oggi, I sistemi agricoli sono chiamati a soddisfare la crescente domanda di cibo e fibre vegetali dovuto al continuo incremento demografico. L’intensificazione di questi sistemi in termini di utilizzo massiccio di fattori produttivi e relative asportazione porta ad aumentare le preoccupazioni in merito all’impatto ambientale. Il principale obiettivo di questo lavoro di tesi è individuare, attraverso prove sperimentali combinate con simulazioni di medio-termine di diversi scenari, la miglior soluzione tecnica in grado di preservare la fertilità del suolo e ridurre l'impatto ambientale del settore agricolo studiando la sinergia tra l'agricoltura conservativa e l'agricoltura di precisione. Considerando i contributi scientifici che studiano i due principi, ed individuati i punti di approfondimento relativi alla gestione della variabilità e mitigazione dell'impatto ambientale, le ipotesi di questo lavoro di tesi sono (1) di mappare la variabilità a livello aziendale in termini di proprietà del suolo e resa in granella allo scopo di definire delle zone omogenee ed attribuirgli un potenziale produttivo; (2) studiare la sinergia tra l’agricoltura conservativa e l’agricoltura di precisione che premette di ottenere incrementi produttivi, energia netta ed efficienza energetica; (3) individuare le migliori strategie derivanti dalla sinergia tra agricoltura conservativa ed agricoltura di precisione, in grado di diminuire nel medio periodo le emissioni di CO2 dei sistemi agricoli usando le simulazioni del modello SALUS. Per verificare queste ipotesi la raccolta dati è stata effettuata utilizzando diverse fonti, approcci e strumenti. Infatti, strumenti per la mappatura del suolo ed il monitoraggio dello stato di vigore delle colture sono stati utilizzati per studiare la variabilità di campo e la sua evoluzione nel tempo per poter definire zone omogenee stabili nel tempo. Inoltre, i modelli di simulazione, quando opportunamente testati, rappresentano un utile strumento per poter definire la miglior strategia gestionale per ottenere delle produzioni sostenibili. Questi trovano diversi campi applicativi, dall’incremento dell’efficienza d’uso dei fattori produttivi alla gestione delle superfici coltivate. La caratterizzazione delle zone omogenee è stata effettuata tramite interpolazione dei dati ARP e dati di resa storici derivanti da mappe di resa. Adottando questo metodo è possibile effettuare analisi su vasta scala. La classificazione e definizione delle zone omogenee è stata ottenuta alimentando un programma geostatistico chiamato MZA con i dati descritti in precedenza. Il numero ottimale di classi omogenee è stato selezionato sulla base di indici derivanti dall’analisi del programma, che per questo studio è risultato essere quattro. Successivamente, il potenziale produttivo di ogni classe omogenea è stato attribuito attraverso analisi della varianza dei dati relativi alle analisi del suolo puntuali e dati di resa storici. Infine, il potenziale produttivo assegnato è stato validato sulla base delle rese medie storiche a livello distrettuale. Per quanto riguarda la resa in granella, nello strip-tillage (ST) e la non lavorazione (NT) si osservano cali del 20% e 15% rispetto alla tecnica convenzionale (CT). Tuttavia, il contributo dell’agricoltura di precisione permette di mitigare questo fenomeno in tutte le tecniche di lavorazione conservativa studiate in questo lavoro. Questo permette di ottenere incrementi produttivi superiori al 10%, che permettono alla minima lavorazione (MT) di eguagliare le rese di CT. Allo stesso modo, MT supportata da agricoltura di precisione raggiunge i più alti valori di energia netta, 2% maggiori di CT. Mentre, l’agricoltura di precisione contribuisce ad aumentare di quasi il 20% l’energia netta in ST e NT rispetto al corrispettivo gestito in modo uniforme. Inoltre, Questa consenta di aumentare l’efficienza energetica in MT e NT del 10% e 2% rispetto a CT. In ST invece, si osservano incrementi del 15% confrontato con la stessa tecnica senza supporto di agricoltura di precisione. D’altronde, i possibili benefici dell’agricoltura di precisione sono stati calcolati in termini di emissioni di carbonio per poter definire le migliori strategie che pesano meno dal punto di vista delle emissioni di CO2 in atmosfera nelle condizioni climatiche attuali. Dalle simulazioni del SALUS si evince che tutte le tesi studiate sono caratterizzate da perdite del contenuto di carbonio organico del suolo (SOC). Tuttavia, si sono registrate minori perdite in MT e NT del 17% e 63% rispetto a CT. Inoltre, l’adozione di tecniche di lavorazione conservativa mitiga anche le emissioni di carbonio legate alle agrotecniche, mentre l’agricoltura di precisione porta ad una ottimizzazione delle risorse esauribili come combustibile fossile e fertilizzanti. Infine, è stato dimostrato che la sinergia tra agricoltura conservativa, specialmente NT, e agricoltura di precisione rappresenta un utile strumento per mitigare le emissioni di carbonio in atmosfera legate all’attività agricola. Infatti, considerando le attuali condizioni climatiche e la variabilità di campo caratterizzante l’area di studio, NT supportata da principi e tecnologie di agricoltura di precisione è in grado di ridurre le emissioni totali annue di CO2 del 56% rispetto a CT.

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Books on the topic "Precision farming"

1

Emmert, Bonnie. Precision farming. Beltsville, Md: National Agricultural Library, 1994.

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Emmert, Bonnie. Precision farming. Beltsville, Md: National Agricultural Library, 1994.

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Emmert, Bonnie. Precision farming. Beltsville, Md: National Agricultural Library, 1994.

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Cox, S., ed. Precision Livestock Farming. The Netherlands: Wageningen Academic Publishers, 2003. http://dx.doi.org/10.3920/978-90-8686-515-4.

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Cox, S., ed. Precision Livestock Farming '05. The Netherlands: Wageningen Academic Publishers, 2005. http://dx.doi.org/10.3920/978-90-8686-548-2.

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Cox, S., ed. Precision livestock farming '07. The Netherlands: Wageningen Academic Publishers, 2007. http://dx.doi.org/10.3920/978-90-8686-604-5.

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Lokhorst, C., and P. W. G. Groot Koerkamp, eds. Precision livestock farming '09. The Netherlands: Wageningen Academic Publishers, 2009. http://dx.doi.org/10.3920/978-90-8686-663-2.

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Halachmi, Ilan, ed. Precision livestock farming applications. The Netherlands: Wageningen Academic Publishers, 2015. http://dx.doi.org/10.3920/978-90-8686-815-5.

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Banhazi, T., V. Halas, and F. Maroto-Molina, eds. Practical Precision Livestock Farming. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-934-3.

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Addicott, James E. The Precision Farming Revolution. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9686-1.

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Book chapters on the topic "Precision farming"

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Singh, Rajesh, Anita Gehlot, Mahesh Kumar Prajapat, and Bhupendra Singh. "Precision Farming." In Artificial Intelligence in Agriculture, 168–76. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003245759-14.

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Ahmad, Latief, and Syed Sheraz Mahdi. "Precision Water Management." In Satellite Farming, 111–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03448-1_8.

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Ahmad, Latief, and Syed Sheraz Mahdi. "Precision Pest Management." In Satellite Farming, 119–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03448-1_9.

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Ahmad, Latief, and Syed Sheraz Mahdi. "Introduction to Precision Agriculture." In Satellite Farming, 1–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03448-1_1.

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Ahmad, Latief, and Syed Sheraz Mahdi. "Components of Precision Agriculture." In Satellite Farming, 19–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03448-1_2.

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Ozguven, Mehmet Metin. "Precision Livestock Farming." In The Digital Age in Agriculture, 29–58. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/b23229-2.

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Addicott, James E. "Farming futures." In The Precision Farming Revolution, 213–29. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9686-1_6.

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Ahmad, Latief, and Syed Sheraz Mahdi. "Recent Advances in Precision Agriculture." In Satellite Farming, 129–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03448-1_10.

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Ahmad, Latief, and Syed Sheraz Mahdi. "Precision Soil Sampling and Tillage." In Satellite Farming, 47–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03448-1_4.

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Griffin, Terry W., Jordan M. Shockley, and Tyler B. Mark. "Economics of Precision Farming." In Precision Agriculture Basics, 221–30. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America, 2018. http://dx.doi.org/10.2134/precisionagbasics.2016.0098.

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Conference papers on the topic "Precision farming"

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Basim, N. Mohammed Abu, G. Abhishek Hariharan, Nishanth Solomon, U. DevaDharshini, N. Rabiya Banu, M. Saranghan, and K. K. Vignajeth. "Autobot for precision farming." In 2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT). IEEE, 2017. http://dx.doi.org/10.1109/icieeimt.2017.8116804.

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Nikhil, Tallam Charan, Tallam Karthik, Tummuri Rajasekhar Reddy, and B. K. Priya. "Agrifucus for Precision Farming." In 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020. http://dx.doi.org/10.1109/iccsp48568.2020.9182357.

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Andonovic, Ivan, Craig Michie, Philippe Cousin, Ahmed Janati, Congduc Pham, and Mamour Diop. "Precision Livestock Farming Technologies." In 2018 Global Internet of Things Summit (GIoTS). IEEE, 2018. http://dx.doi.org/10.1109/giots.2018.8534572.

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Nailwal, Sagar, Raman Chadha, Kunal Chauhan, and Gurpreet Singh. "IoT-based Precision Farming." In 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2023. http://dx.doi.org/10.1109/icimia60377.2023.10426436.

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"Rice precision farming in Korea." In ICT s for Precision Agriculture. Food and Fertilizer Technology Center for the Asian and Pacific Region, 2019. http://dx.doi.org/10.56669/zelq6618.

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Johnson, Richard R., John S. Hickman, and Wayne F. Smith. "Precision Farming in Mechanized Agriculture." In 1997 SAE International Off-Highway and Powerplant Congress and Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1997. http://dx.doi.org/10.4271/972760.

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Nagel, Penelope, and Kim Fleming. "Changing the Cost of Farming: New Tools for Precision Farming." In Thermosense: Thermal Infrared Applications XL, edited by Jaap de Vries and Douglas Burleigh. SPIE, 2018. http://dx.doi.org/10.1117/12.2327023.

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Pino, Miguel, J. P. Matos-Carvalho, Dario Pedro, Luis M. Campos, and Joao Costa Seco. "UAV Cloud Platform for Precision Farming." In 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). IEEE, 2020. http://dx.doi.org/10.1109/csndsp49049.2020.9249551.

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Antonopoulos, Konstantinos, Christos Panagiotou, Christos P. Antonopoulos, and Nikolaos S. Voros. "A-FARM Precision Farming CPS Platform." In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2019. http://dx.doi.org/10.1109/iisa.2019.8900717.

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R J Godwin, G.A.Wood, J.C.Taylor, R. Earl, S. Knight, J. Welsh, and B S Blackmore. "Management Guidelines for Precision Farming : Nitrogen." In 2002 Chicago, IL July 28-31, 2002. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2002. http://dx.doi.org/10.13031/2013.9298.

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Reports on the topic "Precision farming"

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Staenz, K., J. C. Deguise, J. M. Chen, H. McNairn, T. Szeredi, and M. McGovern. The Use of Hyperspectral Data for Precision Farming. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219363.

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McNairn, H., J. C. Deguise, J. Secker, and J. Shang. Development of Remote Sensing Image Products for Use in Precision Farming. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219750.

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Pacheco, A., A. Bannari, J. C. Deguise, H. McNairn, and K. Staenz. Application of Hyperspectral Remote Sensing for LAI Estimation in Precision Farming. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219855.

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McNairn, H., J. C. Deguise, and A. Pacheco. Remote sensing derived products for precision farming: report on results from Clinton '99. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219916.

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McNairn, H., R. J. Brown, M. McGovern, T. Huffman, and J. Ellis. Integration of Multi-Polarized SAR Data and High Spatial Optical Imagery For Precision Farming. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219685.

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Research Institute (IFPRI), International Food Policy. Protected agriculture, precision agriculture, and vertical farming: Brief reviews of issues in the literature focusing on the developing region in Asia. Washington, DC: International Food Policy Research Institute, 2019. http://dx.doi.org/10.2499/p15738coll2.133152.

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Tubb, Catherine, and Tony Seba. Rethinking Food and Agriculture 2020-2030: The Second Domestication of Plants and Animals, the Disruption of the Cow, and the Collapse of Industrial Livestock Farming. RethinkX, September 2019. http://dx.doi.org/10.61322/ijip9096.

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By 2030, the number of cows in the U.S. will have fallen by 50% and the cattle farming industry will be all but bankrupt. All other livestock industries will suffer a similar fate, while the knock-on effects for crop farmers and businesses throughout the value chain will be severe. Rethinking Food and Agriculture shows how the modern food disruption, made possible by rapid advances in precision biology and an entirely new model of production we call Food-as-Software, will have profound implications not just for the industrial agriculture industry, but for the wider economy, society, and the environment.

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Upadhyaya, Shrini K., Abraham Shaviv, Abraham Katzir, Itzhak Shmulevich, and David S. Slaughter. Development of A Real-Time, In-Situ Nitrate Sensor. United States Department of Agriculture, March 2002. http://dx.doi.org/10.32747/2002.7586537.bard.

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Although nitrate fertilizers are critical for enhancing crop production, excess application of nitrate fertilizer can result in ground water contamination leading to the so called "nitrate problem". Health and environmental problems related to this "nitrate problem" have led to serious concerns in many parts of the world including the United States and Israel. These concerns have resulted in legislation limiting the amount of nitrate N in drinking water to 10mg/g. Development of a fast, reliable, nitrate sensor for in-situ application can be extremely useful in dynamic monitoring of environmentally sensitive locations and applying site-specific amounts of nitrate fertilizer in a precision farming system. The long range objective of this study is to develop a fast, reliable, real-time nitrate sensor. The specific objective of this one year feasibility study was to explore the possible use of nitrate sensor based on mid-IR spectroscopy developed at UCD along with the silver halide fiber ATR (i.e. attenuated total internal reflection) sensor developed at TAU to detect nitrate content in solution and soil paste in the presence of interfering compounds. Experiments conducted at Technion and UCD clearly demonstrate the feasibility of detecting nitrate content in solutions as well as soil pastes using mid-IR spectroscopy and an ATR technique. When interfering compounds such as carbonates, bicarbonates, organic matter etc. are present special data analysis technique such as singular value decomposition (SYD) or cross correlation was necessary to detect nitrate concentrations successfully. Experiments conducted in Israel show that silver halide ATR fiber based FEWS, particularly flat FEWS, resulted in low standard error and high coefficient of determination (i.e. R² values) indicating the potential of the flat Fiberoptic Evanescent Wave Spectroscopy (FEWS) for direct determinations of nitrate. Moreover, they found that it was possible to detect nitrate and other anion concentrations using anion exchange membranes and M1R spectroscopy. The combination of the ion-exchange membranes with fiberoptices offers one more option to direct determination of nitrate in environmental systems.

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Agassi, Menahem, Michael J. Singer, Eyal Ben-Dor, Naftaly Goldshleger, Donald Rundquist, Dan Blumberg, and Yoram Benyamini. Developing Remote Sensing Based-Techniques for the Evaluation of Soil Infiltration Rate and Surface Roughness. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7586479.bard.

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The objective of this one-year project was to show whether a significant correlation can be established between the decreasing infiltration rate of the soil, during simulated rainstorm, and a following increase in the reflectance of the crusting soil. The project was supposed to be conducted under laboratory conditions, using at least three types of soils from each country. The general goal of this work was to develop a method for measuring the soil infiltration rate in-situ, solely from the reflectance readings, using a spectrometer. Loss of rain and irrigation water from cultivated fields is a matter of great concern, especially in arid, semi-arid regions, e.g. much of Israel and vast area in US, where water is a limiting factor for crop production. A major reason for runoff of rain and overhead irrigation water is the structural crust that is generated over a bare soils surface during rainfall or overhead irrigation events and reduces its infiltration rate (IR), considerably. IR data is essential for predicting the amount of percolating rainwater and runoff. Available information on in situ infiltration rate and crust strength is necessary for the farmers to consider: when it is necessary to cultivate for breaking the soil crust, crust strength and seedlings emergence, precision farming, etc. To date, soil IR is measured in the laboratory and in small-scale field plots, using rainfall simulators. This method is tedious and consumes considerable resources. Therefore, an available, non-destructive-in situ methods for soil IR and soil crusting levels evaluations, are essential for the verification of infiltration and runoff models and the evaluation of the amount of available water in the soil. In this research, soil samples from the US and Israel were subjected to simulated rainstorms of increasing levels of cumulative energies, during which IR (crusting levels) were measured. The soils from the US were studied simultaneously in the US and in Israel in order to compare the effect of the methodology on the results. The soil surface reflectance was remotely measured, using laboratory and portable spectrometers in the VIS-NIR and SWIR spectral region (0.4-2.5mm). A correlation coefficient spectra in which the wavelength, consisting of the higher correlation, was selected to hold the highest linear correlation between the spectroscopy and the infiltration rate. There does not appear to be a single wavelength that will be best for all soils. The results with the six soils in both countries indeed showed that there is a significant correlation between the infiltration rate of crusted soils and their reflectance values. Regarding the wavelength with the highest correlation for each soil, it is likely that either a combined analysis with more then one wavelength or several "best" wavelengths will be found that will provide useful data on soil surface condition and infiltration rate. The product of this work will serve as a model for predicting infiltration rate and crusting levels solely from the reflectance readings. Developing the aforementioned methodologies will allow increased utilization of rain and irrigation water, reduced runoff, floods and soil erosion hazards, reduced seedlings emergence problems and increased plants stand and yields.

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