In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low...In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low resolution,and significant species variation across different growth stages.To improve the model’s ability to generalize and its robustness,we incorporate target background analysis,data augmentation,and processing techniques like Gaussian noise and brightness adjustment.In target detection,increasing the depth of the neural network can lead to the loss of small target information.To overcome this,we introduce the Space-to-Depth Convolution(SPD-Conv)module into the SPD-YOLOv7 framework,replacing certain convolutional layers in the traditional system backbone and head network.This modification helps retain small target features and location information.Additionally,the Efficient Layer Aggregation Network-Wide(ELAN-W)module is combined with the Convolutional Block Attention Module(CBAM)attention mechanism to extract more efficient features.Experimental results show that the enhanced YOLOv7 model achieves an accuracy of 98.38%,with an average accuracy of 99.4%,outperforming the original YOLOv7 model.These improvements represent an increase of 2.46%in accuracy and 3.19%in average accuracy.The results indicate that the enhanced YOLOv7 model is more efficient and real-time,offering valuable insights for maize pest control.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
Detecting invertebrate pests on crops at early stages is essential for pest management.Traditionally,traps were used to sample pests and then human experts undertook classification and counting to estimate the levels ...Detecting invertebrate pests on crops at early stages is essential for pest management.Traditionally,traps were used to sample pests and then human experts undertook classification and counting to estimate the levels of infestation,which is subjective,error-prone and labour intensive.Recently,semi-automatic pest detection is possible by using computer vision technologies to classify and count pest samples in laboratories or insect traps,however,the decision made by the laboratory-based or trap-based approaches are still too late for more optimised pest management decisions.Today,precision agriculture needs detection of pests on crops so that real-time actions can be taken or optimised decision can be made based on accurate information of time and location pest occurs.In this study,we used computer vision and machine learning technologies to detect invertebrates on crops in the field.We first evaluated the performances of the state-of-art convolutional neural networks(CNNs)and proposed a standard training pipeline.Facing the challenge of rapidly developing comprehensive training data,we used a novel method to generate a virtual database which was successfully used to train a deep residual CNNwith an accuracy of 97.8%in detecting four species of pests in farming environments.The proposed method can be applied to a robotic system for proximal detection of invertebrate pests on crops in real-time.展开更多
文摘In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low resolution,and significant species variation across different growth stages.To improve the model’s ability to generalize and its robustness,we incorporate target background analysis,data augmentation,and processing techniques like Gaussian noise and brightness adjustment.In target detection,increasing the depth of the neural network can lead to the loss of small target information.To overcome this,we introduce the Space-to-Depth Convolution(SPD-Conv)module into the SPD-YOLOv7 framework,replacing certain convolutional layers in the traditional system backbone and head network.This modification helps retain small target features and location information.Additionally,the Efficient Layer Aggregation Network-Wide(ELAN-W)module is combined with the Convolutional Block Attention Module(CBAM)attention mechanism to extract more efficient features.Experimental results show that the enhanced YOLOv7 model achieves an accuracy of 98.38%,with an average accuracy of 99.4%,outperforming the original YOLOv7 model.These improvements represent an increase of 2.46%in accuracy and 3.19%in average accuracy.The results indicate that the enhanced YOLOv7 model is more efficient and real-time,offering valuable insights for maize pest control.
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.
文摘Detecting invertebrate pests on crops at early stages is essential for pest management.Traditionally,traps were used to sample pests and then human experts undertook classification and counting to estimate the levels of infestation,which is subjective,error-prone and labour intensive.Recently,semi-automatic pest detection is possible by using computer vision technologies to classify and count pest samples in laboratories or insect traps,however,the decision made by the laboratory-based or trap-based approaches are still too late for more optimised pest management decisions.Today,precision agriculture needs detection of pests on crops so that real-time actions can be taken or optimised decision can be made based on accurate information of time and location pest occurs.In this study,we used computer vision and machine learning technologies to detect invertebrates on crops in the field.We first evaluated the performances of the state-of-art convolutional neural networks(CNNs)and proposed a standard training pipeline.Facing the challenge of rapidly developing comprehensive training data,we used a novel method to generate a virtual database which was successfully used to train a deep residual CNNwith an accuracy of 97.8%in detecting four species of pests in farming environments.The proposed method can be applied to a robotic system for proximal detection of invertebrate pests on crops in real-time.
