Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.展开更多
The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food.Crop insect detection is a challenging task for farmers as a significant portion of the crops...The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food.Crop insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest attack.Traditional insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features accurately.Experiments were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)model.This paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex background.The 9-fold cross-validation was applied to improve the performance of the classification mod-els.The highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN model.The detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection algo-rithm.The comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the insects.The results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.展开更多
In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot le...In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot learning methods emerged as an attempt to solve this shortcoming.Among the few-shot learning methods,there is a class of methods known as embedding learning or metric learning.These methods tackle the classification problem by learning to compare,needing fewer training data.One of the main problems in plant diseases and pests recognition is the lack of large public datasets available.Due to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning methods.The field is also relevant due to the social and economic importance of agriculture in several countries.In this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks.We achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.展开更多
基金supported by King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSPD2025R697.
文摘Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
文摘The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food.Crop insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest attack.Traditional insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features accurately.Experiments were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)model.This paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex background.The 9-fold cross-validation was applied to improve the performance of the classification mod-els.The highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN model.The detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection algo-rithm.The comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the insects.The results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.
基金R.A.Krohling thanks the Brazilian research agency Conselho Nacional de Desenvolvimento Científico e Tecnólogico(CNPq)for financial support under grant no.304688/2021-5.
文摘In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot learning methods emerged as an attempt to solve this shortcoming.Among the few-shot learning methods,there is a class of methods known as embedding learning or metric learning.These methods tackle the classification problem by learning to compare,needing fewer training data.One of the main problems in plant diseases and pests recognition is the lack of large public datasets available.Due to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning methods.The field is also relevant due to the social and economic importance of agriculture in several countries.In this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks.We achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.
