Littérature scientifique sur le sujet « Early crop mapping »
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Articles de revues sur le sujet "Early crop mapping"
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Kwak, Geun-Ho, Chan-won Park, Kyung-do Lee, Sang-il Na, Ho-yong Ahn et No-Wook Park. « Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data ». Remote Sensing 13, no 9 (21 avril 2021) : 1629. http://dx.doi.org/10.3390/rs13091629.
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When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available.
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Khan, Haseeb Rehman, Zeeshan Gillani, Muhammad Hasan Jamal, Atifa Athar, Muhammad Tayyab Chaudhry, Haoyu Chao, Yong He et Ming Chen. « Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery ». Sensors 23, no 4 (5 février 2023) : 1779. http://dx.doi.org/10.3390/s23041779.
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Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.
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Osman, Julien, Jordi Inglada et Jean-François Dejoux. « Assessment of a Markov logic model of crop rotations for early crop mapping ». Computers and Electronics in Agriculture 113 (avril 2015) : 234–43. http://dx.doi.org/10.1016/j.compag.2015.02.015.
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Hao, Pengyu, Huajun Tang, Zhongxin Chen et Zhengjia Liu. « Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data ». PeerJ 6 (31 août 2018) : e5431. http://dx.doi.org/10.7717/peerj.5431.
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Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.
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Yi, Zhiwei, Li Jia, Qiting Chen, Min Jiang, Dingwang Zhou et Yelong Zeng. « Early-Season Crop Identification in the Shiyang River Basin Using a Deep Learning Algorithm and Time-Series Sentinel-2 Data ». Remote Sensing 14, no 21 (7 novembre 2022) : 5625. http://dx.doi.org/10.3390/rs14215625.
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Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. In recent years, great strides have been made in the development of deep-learning algorithms, and the emergence of Sentinel-2 data with a higher temporal resolution has provided new opportunities for early-season crop identification. In this study, we aimed to fully exploit the potential of deep-learning algorithms and time-series Sentinel-2 data for early-season crop identification and early-season crop mapping. In this study, four classifiers, i.e., two deep-learning algorithms (one-dimensional convolutional networks and long and short-term memory networks) and two shallow machine-learning algorithms (a random forest algorithm and a support vector machine), were trained using early-season Sentinel-2 images and field samples collected in 2019. Then, these algorithms were applied to images and field samples for 2020 in the Shiyang River Basin. Twelve scenarios with different classifiers and time intervals were compared to determine the optimal combination for the earliest crop identification. The results show that: (1) the two deep-learning algorithms outperformed the two shallow machine-learning algorithms in early-season crop identification; (2) the combination of a one-dimensional convolutional network and 5-day interval time-series Sentinel-2 data outperformed the other schemes in obtaining the early-season crop identification time and achieving early mapping; and (3) the early-season crop identification mapping time in the Shiyang River Basin was identified as the end of July, and the overall classification accuracy reached 0.83. In addition, the early identification time for each crop was as follows: the wheat was in the flowering stage (mid-late June); the alfalfa was in the first harvest (mid-late June); the corn was in the early tassel stage (mid-July); the fennel and sunflower were in the flowering stage (late July); and the melons were in the fruiting stage (around late July). This study demonstrates the potential of using Sentinel-2 time-series data and deep-learning algorithms to achieve early-season crop identification, and this method is expected to provide new solutions and ideas for addressing early-season crop identification monitoring.
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Croci, Michele, Giorgio Impollonia, Henri Blandinières, Michele Colauzzi et Stefano Amaducci. « Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy ». Remote Sensing 14, no 18 (11 septembre 2022) : 4540. http://dx.doi.org/10.3390/rs14184540.
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Estimating key crop parameters (e.g., phenology, yield prediction) is a prerequisite for optimizing agrifood supply chains through the use of satellite imagery, but requires timely and accurate crop mapping. The moment in the season and the number of training sites used are two main drivers of crop classification performance. The combined effect of these two parameters was analysed for tomato crop classification, through 125 experiments, using the three main machine learning (ML) classifiers (neural network, random forest, and support vector machine) using a response surface methodology (RSM). Crop classification performance between minority (tomato) and majority (‘other crops’) classes was assessed through two evaluation metrics: Overall Accuracy (OA) and G-Mean (GM), which were calculated on large independent test sets (over 400,000 fields). RSM results demonstrated that lead time and the interaction between the number of majority and minority classes were the two most important drivers for crop classification performance for all three ML classifiers. The results demonstrate the feasibility of preharvest classification of tomato with high performance, and that an RSM-based approach enables the identification of simultaneous effects of several factors on classification performance. SVM achieved the best grading performances across the three ML classifiers, according to both evaluation metrics. SVM reached highest accuracy (0.95 of OA and 0.97 of GM) earlier in the season (low lead time) and with less training sites than the other two classifiers, permitting a reduction in cost and time for ground truth collection through field campaigns.
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Tian, Haifeng, Yongjiu Wang, Ting Chen, Lijun Zhang et Yaochen Qin. « Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery ». Remote Sensing 13, no 19 (24 septembre 2021) : 3822. http://dx.doi.org/10.3390/rs13193822.
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Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types—winter garlic, winter canola and winter wheat—was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing.
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Lussem, U., C. Hütt et G. Waldhoff. « COMBINED ANALYSIS OF SENTINEL-1 AND RAPIDEYE DATA FOR IMPROVED CROP TYPE CLASSIFICATION : AN EARLY SEASON APPROACH FOR RAPESEED AND CEREALS ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (24 juin 2016) : 959–63. http://dx.doi.org/10.5194/isprs-archives-xli-b8-959-2016.
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Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.
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Lussem, U., C. Hütt et G. Waldhoff. « COMBINED ANALYSIS OF SENTINEL-1 AND RAPIDEYE DATA FOR IMPROVED CROP TYPE CLASSIFICATION : AN EARLY SEASON APPROACH FOR RAPESEED AND CEREALS ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (24 juin 2016) : 959–63. http://dx.doi.org/10.5194/isprsarchives-xli-b8-959-2016.
Texte intégralRésumé :
Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.
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HAO, Peng-yu, Hua-jun TANG, Zhong-xin CHEN, Qing-yan MENG et Yu-peng KANG. « Early-season crop type mapping using 30-m reference time series ». Journal of Integrative Agriculture 19, no 7 (juillet 2020) : 1897–911. http://dx.doi.org/10.1016/s2095-3119(19)62812-1.
Texte intégralThèses sur le sujet "Early crop mapping"
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CROCI, MICHELE. « Telerilevamento per il settore agroalimentare : Metodi e Applicazioni ». Doctoral thesis, Università Cattolica del Sacro Cuore, 2022. http://hdl.handle.net/10280/120588.
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Nell'ultimo decennio, il settore agroalimentare e le autorità locali hanno investito in nuove tecnologie per affrontare le crescenti sfide sociali, economiche e ambientali legate al cibo e alla sua sostenibilità. L'intelligenza artificiale (AI) e il Machine Learning (ML) alimentate con dati ad alta risoluzione spaziale e temporale (es. telerilevamento, sensori prossimali, dati meteorologici e mappe del suolo) permettono lo sviluppo di nuovi strumenti in grado di monitorare l'intera catena agroalimentare consentendo a sua volta l'ottimizzazione dei processi produttivi e la loro sostenibilità.
In questo studio sono state indagate le tre principali applicazioni del telerilevamento per il monitoraggio delle colture: i) la mappatura delle colture, ii) la stima dei parametri biofisici e iii) la previsione delle rese. Per ognuna di queste tre applicazioni, è stato analizzato un caso studio al fine di approfondire specifici aspetti metodologici necessari allo sviluppo di un sistema di monitoraggio della filiera agroalimentare.In the last decade, the agri-food sector and local authorities have invested in new technologies to address the growing social, economic, and environmental challenges related to food and its sustainability. Artificial Intelligence (AI) and Machine Learning (ML) fed with high spatial and temporal resolution data (e.g. remote sensing, proximal sensors, weather data and soil maps) enable the development of new tools that can monitor the whole agri-food chain enabling in turn the optimization of production processes and their sustainability. In this study, the three main applications of remote sensing for crop and land monitoring were investigated: i) crop mapping, ii) estimation of biophysical parameters, and iii) yield prediction. In particular, for each of these three applications, some methodological aspects have been analyzed to develop operational solutions for monitoring the agri-food supply chain.
Livres sur le sujet "Early crop mapping"
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Snider, Jill D. Lucean Arthur Headen. University of North Carolina Press, 2020. http://dx.doi.org/10.5149/northcarolina/9781469654355.001.0001.
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Born in Carthage, North Carolina, Lucean Arthur Headen (1879-1957) grew up amid former slave artisans. Inspired by his grandfather, a wheelwright, and great-uncle, a toolmaker, he dreamed as a child of becoming an inventor. His ambitions suffered the menace of Jim Crow and the reality of a new inventive landscape in which investment was shifting from lone inventors to the new “industrial scientists.” But determined and ambitious, Headen left the South, and after toiling for a decade as a Pullman porter, risked everything to pursue his dream. He eventually earned eleven patents, most for innovative engine designs and anti-icing methods for aircraft. An equally capable entrepreneur and sportsman, Headen learned to fly in 1911, manufactured his own “Pace Setter” and “Headen Special” cars in the early 1920s, and founded the first national black auto racing association in 1924, all establishing him as an important authority on transportation technologies among African Americans. Emigrating to England in 1931, Headen also proved a successful manufacturer, operating engineering firms in Surrey that distributed his motor and other products worldwide for twenty-five years. Though Headen left few personal records, Jill D. Snider recreates the life of this extraordinary man through historical detective work in newspapers, business and trade publications, genealogical databases, and scholarly works. Mapping the social networks his family built within the Presbyterian church and other organizations (networks on which Headen often relied), she also reveals the legacy of Carthage's, and the South's, black artisans. Their story shows us that, despite our worship of personal triumph, success is often a communal as well as an individual achievement.
Chapitres de livres sur le sujet "Early crop mapping"
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Singh, Balwant, Shefali Mishra, Deepak Singh Bisht et Rohit Joshi. « Growing Rice with Less Water : Improving Productivity by Decreasing Water Demand ». Dans Rice Improvement, 147–70. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66530-2_5.
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AbstractRice is a staple food for more than half of the global population. With the increasing population, the yield of rice must correspondingly increase to fulfill the requirement. Rice is cultivated worldwide in four different types of ecosystems, which are limited by the availability of irrigation water. However, water-limiting conditions negatively affect rice production; therefore, to enhance productivity under changing climatic conditions, improved cultivation practices and drought-tolerant cultivars/varieties are required. There are two basic approaches to cultivation: (1) plant based and (2) soil and irrigation based, which can be targeted for improving rice production. Crop plants primarily follow three mechanisms: drought escape, avoidance, and tolerance. Based on these mechanisms, different strategies are followed, which include cultivar selection based on yield stability under drought. Similarly, soil- and irrigation-based strategies consist of decreasing non-beneficial water depletions and water outflows, aerobic rice development, alternate wetting and drying, saturated soil culture, system of rice intensification, and sprinkler irrigation. Further strategies involve developing drought-tolerant cultivars through marker-assisted selection/pyramiding, genomic selection, QTL mapping, and other breeding and cultivation practices such as early planting to follow escape strategies and decreasing stand density to minimize competition with weeds. Similarly, the identification of drought-responsive genes and their manipulation will provide a technological solution to overcome drought stress. However, it was the Green Revolution that increased crop production. To maintain the balance, there is a need for another revolution to cope with the increasing demand.
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Adhikary, Saju, Benukar Biswas, Manish Kumar Naskar, Bishal Mukherjee, Aditya Pratap Singh et Kousik Atta. « Remote Sensing for Agricultural Applications ». Dans Arid Environment - Perspectives, Challenges and Management [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.106876.
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The application of remote sensing in quantifying the crop health status is trending. Sensors can serve as early warning systems for countering climatic or biological aberrations before having negative impacts on crop yield. Remote sensing applications have been playing a significant role in agriculture sector for evaluating plant health, yield and crop loss (%) estimation, irrigation management, identification of crop stress, weed and pest detection, weather forecasting, gathering crop phenological informations etc. Forecast of crop yields by using remote sensing inputs in conjunction with crop simulation models is getting popular day by day for its potential benefits. Remote sensing reduces the amount of field data collection and improves the precision of the estimates. Crop stress caused by biotic and abiotic factors can be monitored and quantified with remote sensing. Monitoring of vegetation cover for acreage estimation, mapping and monitoring drought condition and maintenance of vegetation health, assessment of crop condition under stress prone environment, checking of nutrient and moisture status of field, measurement of crop evapotranspiration, weed management through precision agriculture, gathering and transferring predictions of atmospheric dynamics through different observational satellites are the major agricultural applications of remote sensing technologies. Normalized difference vegetation index (NDVI), vegetation condition index (VCI), leaf area index (LAI), and General Yield Unified Reference Index (GYURI) are some of the indices which have been used for mapping and monitoring drought and assessing vegetation health and productivity. Remote sensing with other advanced technologies like geographical information systems (GIS) are playing a massive role in assessment and management of several agricultural activities. State or district level information systems based on available remote sensing information are required to be utilized efficiently for improving the economy coming from agriculture.
Actes de conférences sur le sujet "Early crop mapping"
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Mohite, Jayantrao, Suryakant Sawant, Ankur Pandit et Srinivasu Pappula. « Integration of Sentinel 1 and 2 Observations for Mapping Early and Late Sowing of Soybean and Cotton Crop Using Deep Learning ». Dans IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323482.
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Savidis, Anthony, et Anthony Peris. « Rapid Interactive Software-Architecture Design with Split-n-Join Actions ». Dans 8th International Conference on Human Interaction and Emerging Technologies. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002770.
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The architecture design process is an essential and critical part of the overall software development lifecycle. During the early design phases it is imperative to ensure that an optimal architecture is outlined, reflecting the requirements of the target software product. Then, continuous refinement, syncing and maintenance is needed, in order to guarantee that the software architecture precisely reflects the particular state of the source code base and vice versa. Both processes involve elements and activities at an abstract level and require support for easy and quick experimentation, exploration and prototyping. In existing tools, commonly relying on UML diagrams, the architecture design process is very detailed and thus time consuming, asking designers to elaborate early on aspects that are usually finalized latter in the process. Effectively, such tools are not interactive prototyping laboratories, but are primarily architecture documentation environments. However, because they require so fine-grained detail, which is transient, volatile and non-final in the early design phases, they are less preferred for initial experimentation and analysis. Effectively, it is impractical for architects to spend the required effort in supplying data for components, specifications and relationships when those frequently change in the early design process.Based on these remarks, our work focuses on supporting the very early phases of the architecture design process, putting primary emphasis on rapid interactive construction, ease-of-use, continuous experimentation, minimal information, and adoption of common architectural abstractions. While our tool focuses on components, it reflects the exploratory nature of the design process by offering two key actions, namely splitting and joining components, besides typical creation and removal. Our work is inspired by the quick class design method known as CRC Cards (Classes, Responsibilities and Collaborators), part of agile development, by adapting the original notions to fit with the scale and abstractions of the software architecture domain as Components, Roles, Operations and Synergies (CROS).In our tool, the primary requirement has been the facilitation of rapid exploratory interactive design, with small effort on behalf of the user, making it a laboratory for testing where related ideas may be easily instantiated via the tool. Considering that the architecture structure changes frequently in this process, we identified most common actions architects perform when revisiting component roles, besides component insertion and removal: •Splitting: when a particular component is identified that blends many different disciplines together that deserve representation (i.e. decomposition) into distinct and separate components;•Joining: when a few components are considered as weak or arbitrary to stand on the own, while in terms of their functional role they look as pieces of the same concept, likely requiring merging together under the same umbrella.•Mapping: when the high-level functions that are typically identified following the requirement analysis process should be mapped to components in a way better matching its functional role – such mapping may change as well, while new operations may be introduced in the process.We discuss how such simple activities are fundamental and capture the essential aspects the early architecture design tasks, and the way we supported interactively such key tasks, while keeping their delivery simple, quick and yet sufficient. For instance, component associations or synergies may change by simply rearranging links with the mouse, while operations are managed easily by typical drag-n-drop. Additionally, further component decomposition is supported, enabling craft quickly the sub-architecture of any selected component. Notes may be freely added to components, while their view may be toggled with just a click.
Rapports d'organisations sur le sujet "Early crop mapping"
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Lee, W. S., Victor Alchanatis et Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, janvier 2014. http://dx.doi.org/10.32747/2014.7598158.bard.
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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Zhang, Hongbin B., David J. Bonfil et Shahal Abbo. Genomics Tools for Legume Agronomic Gene Mapping and Cloning, and Genome Analysis : Chickpea as a Model. United States Department of Agriculture, mars 2003. http://dx.doi.org/10.32747/2003.7586464.bard.
Texte intégralRésumé :
The goals of this project were to develop essential genomic tools for modern chickpea genetics and genomics research, map the genes and quantitative traits of importance to chickpea production and generate DNA markers that are well-suited for enhanced chickpea germplasm analysis and breeding. To achieve these research goals, we proposed the following research objectives in this period of the project: 1) Develop an ordered BAC library with an average insert size of 150 - 200 kb (USA); 2) Develop 300 simple sequence repeat (SSR) markers with an aid of the BAC library (USA); 3) Develop SSR marker tags for Ascochyta response, flowering date and grain weight (USA); 4) Develop a molecular genetic map consisting of at least 200 SSR markers (Israel and USA); 5) Map genes and QTLs most important to chickpea production in the U.S. and Israel: Ascochyta response, flowering and seed set date, grain weight, and grain yield under extreme dryland conditions (Israel); and 6) Determine the genetic correlation between the above four traits (Israel). Chickpea is the third most important pulse crop in the world and ranks the first in the Middle East. Chickpea seeds are a good source of plant protein (12.4-31.5%) and carbohydrates (52.4-70.9%). Although it has been demonstrated in other major crops that the modern genetics and genomics research is essential to enhance our capacity for crop genetic improvement and breeding, little work was pursued in these research areas for chickpea. It was absent in resources, tools and infrastructure that are essential for chickpea genomics and modern genetics research. For instance, there were no large-insert BAC and BIBAC libraries, no sufficient and user- friendly DNA markers, and no intraspecific genetic map. Grain sizes, flowering time and Ascochyta response are three main constraints to chickpea production in drylands. Combination of large seeds, early flowering time and Ascochyta blight resistance is desirable and of significance for further genetic improvement of chickpea. However, it was unknown how many genes and/or loci contribute to each of the traits and what correlations occur among them, making breeders difficult to combine these desirable traits. In this period of the project, we developed the resources, tools and infrastructure that are essential for chickpea genomics and modern genetics research. In particular, we constructed the proposed large-insert BAC library and an additional plant-transformation-competent BIBAC library from an Israeli advanced chickpea cultivar, Hadas. The BAC library contains 30,720 clones and has an average insert size of 151 kb, equivalent to 6.3 x chickpea haploid genomes. The BIBAC library contains 18,432 clones and has an average insert size of 135 kb, equivalent to 3.4 x chickpea haploid genomes. The combined libraries contain 49,152 clones, equivalent to 10.7 x chickpea haploid genomes. We identified all SSR loci-containing clones from the chickpea BAC library, generated sequences for 536 SSR loci from a part of the SSR-containing BACs and developed 310 new SSR markers. From the new SSR markers and selected existing SSR markers, we developed a SSR marker-based molecular genetic map of the chickpea genome. The BAC and BIBAC libraries, SSR markers and the molecular genetic map have provided essential resources and tools for modern genetic and genomic analyses of the chickpea genome. Using the SSR markers and genetic map, we mapped the genes and loci for flowering time and Ascochyta responses; one major QTL and a few minor QTLs have been identified for Ascochyta response and one major QTL has been identified for flowering time. The genetic correlations between flowering time, grain weight and Ascochyta response have been established. These results have provided essential tools and knowledge for effective manipulation and enhanced breeding of the traits in chickpea.
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Feldman, Moshe, Eitan Millet, Calvin O. Qualset et Patrick E. McGuire. Mapping and Tagging by DNA Markers of Wild Emmer Alleles that Improve Quantitative Traits in Common Wheat. United States Department of Agriculture, février 2001. http://dx.doi.org/10.32747/2001.7573081.bard.
Texte intégralRésumé :
The general goal was to identify, map, and tag, with DNA markers, segments of chromosomes of a wild species (wild emmer wheat, the progenitor of cultivated wheat) determining the number, chromosomal locations, interactions, and effects of genes that control quantitative traits when transferred to a cultivated plant (bread wheat). Slight modifications were introduced and not all objectives could be completed within the human and financial resources available, as noted with the specific objectives listed below: 1. To identify the genetic contribution of each of the available wild emmer chromosome-arm substitution lines (CASLs) in the bread wheat cultivar Bethlehem for quantitative traits, including grain yield and its components and grain protein concentration and yield, and the effect of major loci affecting the quality of end-use products. [The quality of end-use products was not analyzed.] 2. To determine the extent and nature of genetic interactions (epistatic effects) between and within homoeologous groups 1 and 7 for the chromosome arms carrying "wild" and "cultivated" alleles as expressed in grain and protein yields and other quantitative traits. [Two experiments were successful, grain protein concentration could not be measured; data are partially analyzed.] 3. To derive recombinant substitution lines (RSLs) for the chromosome arms of homoeologous groups 1 and 7 that were found previously to promote grain and protein yields of cultivated wheat. [The selection of groups 1 and 7 tons based on grain yield in pot experiments. After project began, it was decided also to derive RSLs for the available arms of homoeologous group 4 (4AS and 4BL), based on the apparent importance of chromosome group 4, based on early field trials of the CASLs.] 4. To characterize the RSLs for quantitative traits as in objective 1 and map and tag chromosome segments producing significant effects (quantitative trait loci, QTLs by RFLP markers. [Producing a large population of RSLs for each chromosome arm and mapping them proved more difficult than anticipated, low numbers of RSLs were obtained for two of the chromosome arms.] 5. To construct recombination genetic maps of chromosomes of homoeologous groups 1 and 7 and to compare them to existing maps of wheat and other cereals [Genetic maps are not complete for homoeologous groups 4 and 7.] The rationale for this project is that wild species have characteristics that would be valuable if transferred to a crop plant. We demonstrated the sequence of chromosome manipulations and genetic tests needed to confirm this potential value and enhance transfer. This research has shown that a wild tetraploid species harbors genetic variability for quantitative traits that is interactive and not simply additive when introduced into a common genetic background. Chromosomal segments from several chromosome arms improve yield and protein in wheat but their effect is presumably enhanced when combination of genes from several segments are integrated into a single genotype in order to achieve the benefits of genes from the wild species. The interaction between these genes and those in the recipient species must be accounted for. The results of this study provide a scientific basis for some of the disappointing results that have historically obtained when using wild species as donors for crop improvement and provide a strategy for further successes.