Crop diseases pose a critical threat to global food security.Traditional diagnostic methods are inefficient and fail to meet the demands of modern precision agriculture.In recent years,artificial intelligence(AI)techn...Crop diseases pose a critical threat to global food security.Traditional diagnostic methods are inefficient and fail to meet the demands of modern precision agriculture.In recent years,artificial intelligence(AI)technologies centered on deep learning have revolutionized the rapid and precise identification of crop diseases.This paper systematically outlines key AI techniques for crop disease recognition,including computer vision-based image recognition,multimodal data fusion,and edge computing for field deployment.By analyzing representative domestic and international application cases,this paper highlights the significant advantages of this technology in terms of accuracy and efficiency.Simultaneously,it delves into current technical bottlenecks and deployment barriers,such as the few-shot learning problem,environmental interference,and low farmer trust.The paper concludes by outlining future directions,including self-supervised learning,digital twins,and industry integration,to advance the deep application and implementation of AI technology in smart agriculture.展开更多
With the development of the international trade and agricultural science and technology, especially after the execution of the rules on protection of new plant varieties, considerable emphasis has been placed on varie...With the development of the international trade and agricultural science and technology, especially after the execution of the rules on protection of new plant varieties, considerable emphasis has been placed on variety identification. Many evidences have suggested that gel electrophoresis have great influence on this area. This paper reviewed study status of various gel electrophoresis, including development of the meth-展开更多
Accurately predicting crop cultivation information in the early stages is important for national food security decision-making.However,due to limited time-series observation,early crop mapping is a difficult task.The ...Accurately predicting crop cultivation information in the early stages is important for national food security decision-making.However,due to limited time-series observation,early crop mapping is a difficult task.The existing works focus only on feature modeling,relying on uncertain time-series observations,which have been proved not to be a promising direction.Crop cultivation has a regular and cyclical pattern,which could be used to guide crop identification for the upcoming year.Building upon this,a Bayesian probabilistic updating model(BPUM)is proposed for early crop identification.The key of BPUM is iteratively optimizing the crop cultivation probability based on all possible knowledge and observations.Firstly,historical cultivation knowledge can be modeled by estimating the prior probability distribution.Meanwhile,BPUM designs to integrate prior probability and new stage observation.Furthermore,every new stage observation could contribute to updating this prior probability distribution.With the increase in observations,the intelligence of the model can be enhanced.Experiments were conducted in 2 study areas with different climatic conditions.The results indicate that this approach can identify crops 1 to 2 months earlier than traditional methods with overall accuracy of 94.66% and 96.00% at these areas and is applicable to various agricultural regions,demonstrating good stability and applicability.展开更多
How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness ...How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer （MODIS） surface reflectance product （MOD09A1） data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index （SI） was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index （NDTI）, land surface water index （LSWl） and enhanced vegetation index （EVI） are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy （producers＇ accuracy of 94.03% and users＇ accuracy of 93.77%） with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.展开更多
Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield.The present study introduces a reliable deep learning platform called"Dee...Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield.The present study introduces a reliable deep learning platform called"Deep Learning-Crop Platform"(DL-CRoP)for the identification of some commercially grown plants and their nutrient requirements using leaf,stem,and root images using a convolutional neural network(CNN).It extracts intrinsic feature patterns through hierarchical mapping and provides remarkable outcomes in identification tasks.The DL-CRoP platform is trained on the plant image dataset,namely,Jammu University-Botany Image Database(JU-BID),available at https://github.com/urfanbutt.The findings demonstrate implementation of DL-CRoP-cases A(uses shoot images)and B(uses leaf images)for species identification for Solanum lycopersicum(tomato),Vigna radiata(Vigna),and Zea mays(maize),and cases C(uses leaf images)and D(uses root images)for diagnosis of nitrogen deficiency in maize.The platform achieved a higher rate of accuracy at 80-20,70-30,and 60-40 splits for all the case studies,compared with established algorithms such as random forest,K-nearest neighbor,support vector machine,AdaBoost,and naive Bayes.It provides a higher accuracy rate in classification parameters like recall,precision,and F1 score for cases A(90.45%),B(100%),and C(93.21),while a medium-level accuracy of 68.54%for case D.To further improve the accuracy of the platform in case study C,the CNN was modified including a multi-head attention(MHA)block.It resulted in the enhancement of the accuracy of classifying the nitrogen deficiency above 95%.The platform could play an important role in evaluating the health status of crop plants along with a role in precise identification of species.It may be used as a better module for precision crop cultivation under limited nutrient conditions.展开更多
Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free lab...Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free labeled datasets.This paper introduces DAClA5,a multispectral image dataset for agricultural crop identification,complemented with Sentinel-1 radar data.The dataset consists of 172 Sentinel-2 multispectral images(800×450 pixels)and 159 Sentinel-1 radar images,collected over Braov,Romania,from 2020 to 2024,with precise,in-situ verified labels.Additionally,6,454 Sentinel-2 and 5,995 Sentinel-1 rectangular patches(32 x 32 pixels)were extracted,exceeding 6 million pixels in total.The cropland parcels considered in our dataset are used for research and are owned and cultivated by the National Institute of Research and Development for Potato and Sugar Beet,ensuring error-free labeling.The labels in our dataset provide detailed information about crop types,offering insights into crop distribution,growth stages,and phenological events.Furthermore,we present a comprehensive dataset analysis and two key use cases:crop identifi cation based on a"past vs.present"approach and early crop identification during the agricultural season.展开更多
Soybean and maize are important raw materials for the production of food and livestock feed.Accurate mapping of these two crops is of great significance to crop management,yield estimation,and crop-damage control.In t...Soybean and maize are important raw materials for the production of food and livestock feed.Accurate mapping of these two crops is of great significance to crop management,yield estimation,and crop-damage control.In this study,two towns in Guoyang County,Anhui Province,China,were selected as the study area,and Sentinel-2 images were adopted to map the distributions of both crops in the 2019 growing season.The data obtained on August 18(early pod-setting stage of soybean)was determined to be the most applicable to soybean and maize mapping by means of the Jeffries-Matusita(JM)distance.Subsequently,three machine-learning algorithms,i.e.,random forest(RF),support vector machine(SVM)and back-propagation neural network(BPNN)were employed and their respective performance in crop identification was evaluated with the aid of 254 ground truth plots.It appeared that RF with a Kappa of 0.83 was superior to the other two methods.Furthermore,twenty candidate features containing the reflectance of ten spectral bands(spatial resolution at 10 m or 20 m)and ten remote-sensing indices were input into the RF algorithm to conduct an important assessment.Seven features were screened out and served as the optimum subset,the mapping results of which were assessed based on the ground truth derived from the unmanned aerial vehicle(UAV)images covering six ground samples.The optimum feature-subset achieved high-accuracy crop mapping,with a reduction of data volume by 65%compared with the total twenty features,which also overrode the performance of ten spectral bands.Therefore,feature-optimization had great potential in the identification of the two crops.Generally,the findings of this study can provide a valuable reference for mapping soybean and maize in areas with a fragmented landscape of farmland and complex planting structure.展开更多
This paper describes the design,algorithm development,and experimental verification of a precise spray perception system based on LiDAR were presented to address the issue that the navigation line extraction accuracy ...This paper describes the design,algorithm development,and experimental verification of a precise spray perception system based on LiDAR were presented to address the issue that the navigation line extraction accuracy of self-propelled sprayers during field operations is low,resulting in wheels rolling over the ridges and excessive pesticide waste.A data processing framework was established for the precision spray perception system.Through data preprocessing,adaptive segmentation of crops and ditches,extraction of navigation lines and crop positioning,which were derived from the original LiDAR point cloud species.Data collection and analysis of the field environment of cabbages in different growth cycles were conducted to verify the stability of the precision spraying system.A controllable constant-speed experimental setup was established to compare the performance of LiDAR and depth camera in the same field environment.The experimental results show that at the self-propelled sprayer of speeds of 0.5 and 1 ms−1,the maximum lateral error is 0.112 m in a cabbage ridge environment with inter-row weeds,with an mean absolute lateral error of 0.059 m.The processing speed per frame does not exceed 43 ms.Compared to the machine vision algorithm,this method reduces the average processing time by 122 ms.The proposed system demonstrates superior accuracy,processing time,and robustness in crop identification and navigation line extraction compared to the machine vision system.展开更多
Estimation of damage in plants is a key issue for crop protection.Currently,experts in the field manually assess the plots.This is a time-consuming task that can be automated thanks to the latest technology in compute...Estimation of damage in plants is a key issue for crop protection.Currently,experts in the field manually assess the plots.This is a time-consuming task that can be automated thanks to the latest technology in computer vision(CV).The use of image-based systems and recently deep learning-based systems have provided good results in several agricultural applications.These image-based applications outperform expert evaluation in controlled environments,and now they are being progressively included in non-controlled field applications.A novel solution based on deep learning techniques in combination with image processingmethods is proposed to tackle the estimate of plant damage in the field.The proposed solution is a two-stage algorithm.In a first stage,the single plants in the plots are detected by an object detection YOLO based model.Then a regression model is applied to estimate the damage of each individual plant.The solution has been developed and validated in oilseed rape plants to estimate the damage caused by flea beetle.The crop detection model achieves a mean precision average of 91%with a mAP@0.50 of 0.99 and a mAP@0.95 of 0.91 for oilseed rape specifically.The regression model to estimate up to 60%of damage degree in single plants achieves a MAE of 7.11,and R2 of 0.46 in comparison with manual evaluations done plant by plant by experts.Models are deployed in a docker,and with a REST API communication protocol they can be inferred directly for images acquired in the field from a mobile device.展开更多
文摘Crop diseases pose a critical threat to global food security.Traditional diagnostic methods are inefficient and fail to meet the demands of modern precision agriculture.In recent years,artificial intelligence(AI)technologies centered on deep learning have revolutionized the rapid and precise identification of crop diseases.This paper systematically outlines key AI techniques for crop disease recognition,including computer vision-based image recognition,multimodal data fusion,and edge computing for field deployment.By analyzing representative domestic and international application cases,this paper highlights the significant advantages of this technology in terms of accuracy and efficiency.Simultaneously,it delves into current technical bottlenecks and deployment barriers,such as the few-shot learning problem,environmental interference,and low farmer trust.The paper concludes by outlining future directions,including self-supervised learning,digital twins,and industry integration,to advance the deep application and implementation of AI technology in smart agriculture.
基金supported by NSFC(39970444)the National“973”Fundamental Research Program(G1998010202).
文摘With the development of the international trade and agricultural science and technology, especially after the execution of the rules on protection of new plant varieties, considerable emphasis has been placed on variety identification. Many evidences have suggested that gel electrophoresis have great influence on this area. This paper reviewed study status of various gel electrophoresis, including development of the meth-
基金supported in part by the National Key R&D Program of China under Grant 2022YFB3903402the National Natural Science Foundation of China under Grants 42222106 and 61976234.
文摘Accurately predicting crop cultivation information in the early stages is important for national food security decision-making.However,due to limited time-series observation,early crop mapping is a difficult task.The existing works focus only on feature modeling,relying on uncertain time-series observations,which have been proved not to be a promising direction.Crop cultivation has a regular and cyclical pattern,which could be used to guide crop identification for the upcoming year.Building upon this,a Bayesian probabilistic updating model(BPUM)is proposed for early crop identification.The key of BPUM is iteratively optimizing the crop cultivation probability based on all possible knowledge and observations.Firstly,historical cultivation knowledge can be modeled by estimating the prior probability distribution.Meanwhile,BPUM designs to integrate prior probability and new stage observation.Furthermore,every new stage observation could contribute to updating this prior probability distribution.With the increase in observations,the intelligence of the model can be enhanced.Experiments were conducted in 2 study areas with different climatic conditions.The results indicate that this approach can identify crops 1 to 2 months earlier than traditional methods with overall accuracy of 94.66% and 96.00% at these areas and is applicable to various agricultural regions,demonstrating good stability and applicability.
基金financially supported by the Non-Profit Research Grant of the National Administration of Surveying,Mapping and Geoinformation of China (201512028)the National Natural Science Foundation of China (41271112)
文摘How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer （MODIS） surface reflectance product （MOD09A1） data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index （SI） was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index （NDTI）, land surface water index （LSWl） and enhanced vegetation index （EVI） are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy （producers＇ accuracy of 94.03% and users＇ accuracy of 93.77%） with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.
基金funded by a CSIR-SRF fellowship to M.U.and the Department of Science and Technology-Science and Engineering Research Board(DST-SERB)Core Research Grant(CRG)to S.P.C.
文摘Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield.The present study introduces a reliable deep learning platform called"Deep Learning-Crop Platform"(DL-CRoP)for the identification of some commercially grown plants and their nutrient requirements using leaf,stem,and root images using a convolutional neural network(CNN).It extracts intrinsic feature patterns through hierarchical mapping and provides remarkable outcomes in identification tasks.The DL-CRoP platform is trained on the plant image dataset,namely,Jammu University-Botany Image Database(JU-BID),available at https://github.com/urfanbutt.The findings demonstrate implementation of DL-CRoP-cases A(uses shoot images)and B(uses leaf images)for species identification for Solanum lycopersicum(tomato),Vigna radiata(Vigna),and Zea mays(maize),and cases C(uses leaf images)and D(uses root images)for diagnosis of nitrogen deficiency in maize.The platform achieved a higher rate of accuracy at 80-20,70-30,and 60-40 splits for all the case studies,compared with established algorithms such as random forest,K-nearest neighbor,support vector machine,AdaBoost,and naive Bayes.It provides a higher accuracy rate in classification parameters like recall,precision,and F1 score for cases A(90.45%),B(100%),and C(93.21),while a medium-level accuracy of 68.54%for case D.To further improve the accuracy of the platform in case study C,the CNN was modified including a multi-head attention(MHA)block.It resulted in the enhancement of the accuracy of classifying the nitrogen deficiency above 95%.The platform could play an important role in evaluating the health status of crop plants along with a role in precise identification of species.It may be used as a better module for precision crop cultivation under limited nutrient conditions.
基金Funded by the European UnionThe Al4AGRl project entitled"Romanian Excellence Center on Artificial Intelligence on Earth Observation Data for Agriculture"received funding from the European Union's Horizon Europe research and innovation program under grant agreement no.101079136Al4AGRl project received funding from the European Union's Horizon Europe research and innovation programme[101079136].
文摘Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free labeled datasets.This paper introduces DAClA5,a multispectral image dataset for agricultural crop identification,complemented with Sentinel-1 radar data.The dataset consists of 172 Sentinel-2 multispectral images(800×450 pixels)and 159 Sentinel-1 radar images,collected over Braov,Romania,from 2020 to 2024,with precise,in-situ verified labels.Additionally,6,454 Sentinel-2 and 5,995 Sentinel-1 rectangular patches(32 x 32 pixels)were extracted,exceeding 6 million pixels in total.The cropland parcels considered in our dataset are used for research and are owned and cultivated by the National Institute of Research and Development for Potato and Sugar Beet,ensuring error-free labeling.The labels in our dataset provide detailed information about crop types,offering insights into crop distribution,growth stages,and phenological events.Furthermore,we present a comprehensive dataset analysis and two key use cases:crop identifi cation based on a"past vs.present"approach and early crop identification during the agricultural season.
基金This work was supported by the Key Project of Natural Science Research of Education Department of Anhui Province(Grant No.KJ2019A0120)the National Key Research and Development Program of China(2019YFE0115200)+2 种基金and the Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis&Application(Grant No.AE2018011)The authors appreciate the European Space Agency(ESA)which provides Sentinel-2 images for this researchthe editor and anonymous reviewers for their valuable comments that helped us to improve this manuscript.
文摘Soybean and maize are important raw materials for the production of food and livestock feed.Accurate mapping of these two crops is of great significance to crop management,yield estimation,and crop-damage control.In this study,two towns in Guoyang County,Anhui Province,China,were selected as the study area,and Sentinel-2 images were adopted to map the distributions of both crops in the 2019 growing season.The data obtained on August 18(early pod-setting stage of soybean)was determined to be the most applicable to soybean and maize mapping by means of the Jeffries-Matusita(JM)distance.Subsequently,three machine-learning algorithms,i.e.,random forest(RF),support vector machine(SVM)and back-propagation neural network(BPNN)were employed and their respective performance in crop identification was evaluated with the aid of 254 ground truth plots.It appeared that RF with a Kappa of 0.83 was superior to the other two methods.Furthermore,twenty candidate features containing the reflectance of ten spectral bands(spatial resolution at 10 m or 20 m)and ten remote-sensing indices were input into the RF algorithm to conduct an important assessment.Seven features were screened out and served as the optimum subset,the mapping results of which were assessed based on the ground truth derived from the unmanned aerial vehicle(UAV)images covering six ground samples.The optimum feature-subset achieved high-accuracy crop mapping,with a reduction of data volume by 65%compared with the total twenty features,which also overrode the performance of ten spectral bands.Therefore,feature-optimization had great potential in the identification of the two crops.Generally,the findings of this study can provide a valuable reference for mapping soybean and maize in areas with a fragmented landscape of farmland and complex planting structure.
基金funded by the Hunan Provincial Science and Technology Department of Major Project-Ten Major Technical Research Projects(2023NK1020)the Hunan Provincial Department of Science and Technology Key Areas R&D Program(2023NK2010)+5 种基金the Hunan Provincial Department of Education Key Projects(299054)the Chenzhou National Sustainable Development Agenda Innovation Demonstration Zone Construction Special Project(2022sfq20)the National Key R&D Program(2022YFD2002001)supported by the 2023 High-level Guangdong Agricultural Science and Technology Demonstration City Construction Fund MunicipalAcademy Cooperation Project(2320060002384)Changsha Science and Technology Bureau Natural Science Foundation Project(kq2402110)the 2023 Intelligent Agricultural Machinery Equipment Innovation Research and Development Project entitled“Southern Paddy Field Green Manure Ditcher R&D,Manufacturing,Promotion and Application”in Hunan Province.
文摘This paper describes the design,algorithm development,and experimental verification of a precise spray perception system based on LiDAR were presented to address the issue that the navigation line extraction accuracy of self-propelled sprayers during field operations is low,resulting in wheels rolling over the ridges and excessive pesticide waste.A data processing framework was established for the precision spray perception system.Through data preprocessing,adaptive segmentation of crops and ditches,extraction of navigation lines and crop positioning,which were derived from the original LiDAR point cloud species.Data collection and analysis of the field environment of cabbages in different growth cycles were conducted to verify the stability of the precision spraying system.A controllable constant-speed experimental setup was established to compare the performance of LiDAR and depth camera in the same field environment.The experimental results show that at the self-propelled sprayer of speeds of 0.5 and 1 ms−1,the maximum lateral error is 0.112 m in a cabbage ridge environment with inter-row weeds,with an mean absolute lateral error of 0.059 m.The processing speed per frame does not exceed 43 ms.Compared to the machine vision algorithm,this method reduces the average processing time by 122 ms.The proposed system demonstrates superior accuracy,processing time,and robustness in crop identification and navigation line extraction compared to the machine vision system.
文摘Estimation of damage in plants is a key issue for crop protection.Currently,experts in the field manually assess the plots.This is a time-consuming task that can be automated thanks to the latest technology in computer vision(CV).The use of image-based systems and recently deep learning-based systems have provided good results in several agricultural applications.These image-based applications outperform expert evaluation in controlled environments,and now they are being progressively included in non-controlled field applications.A novel solution based on deep learning techniques in combination with image processingmethods is proposed to tackle the estimate of plant damage in the field.The proposed solution is a two-stage algorithm.In a first stage,the single plants in the plots are detected by an object detection YOLO based model.Then a regression model is applied to estimate the damage of each individual plant.The solution has been developed and validated in oilseed rape plants to estimate the damage caused by flea beetle.The crop detection model achieves a mean precision average of 91%with a mAP@0.50 of 0.99 and a mAP@0.95 of 0.91 for oilseed rape specifically.The regression model to estimate up to 60%of damage degree in single plants achieves a MAE of 7.11,and R2 of 0.46 in comparison with manual evaluations done plant by plant by experts.Models are deployed in a docker,and with a REST API communication protocol they can be inferred directly for images acquired in the field from a mobile device.
