Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminar...Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops.Manually identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations.An atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural production.This paper aims to identify pests in mango trees such as hoppers,mealybugs,inflorescence midges,fruitflies,and stem borers.Because of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing images.The community-based cumulative algorithm was used to classify the pests in the existing system.The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agricul-ture.The Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classification accuracy.Support Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are interested.They are created as suitable classifiers to categorize any dataset in Big Data effectively.The proposed Entropy-ELM-WOA is more capable compared to the existing systems.展开更多
The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on ...The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on their early identification and accurate classification.The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees.Our study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf Blight.To maintain image quality and consistency,pre-processing techniques were employed.We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset.The customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification accuracy.This result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease diagnosis.Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models.Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees.These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.展开更多
This study was conducted in the Korhogo region in an age gradient of mango orchards. The goal was to assess diversity and determine the influence of the environment on the composition of these organisms. Sampling was ...This study was conducted in the Korhogo region in an age gradient of mango orchards. The goal was to assess diversity and determine the influence of the environment on the composition of these organisms. Sampling was done using pitfall traps, threshing and manual collection. A total of 82 ant species were sampled on all plots. The cultivated plots are richer in ant species than the natural environment. Of the three (3) sampling methods, the Manual Collection proved to be more efficient (eff = 79.52%), followed by threshing (eff = 75.15%) and finally the least effective trap pit (eff = 73.39%). The number of individuals of the species varies according to the environment. The highest value was obtained in the young plot (485 individuals), followed by the intermediate plot (478 individuals), then the older plot (426 individuals) and finally the control plot (320 individuals). The species Camponotus acvapimensis was harvested mainly with 159 individuals. On the other hand, the species Lepisiota sp.1;Camponotus rufigenis, Camponotus sericeus and Oecophylla longinoda were specifically harvested in the control, young, intermediate and aged plots, respectively.展开更多
为了准确地识别和定位自然环境中接近成熟或已成熟的树上芒果,提出了一种基于改进YOLOv3的目标检测方法(ISD-YOLOv3)。该方法首先利用在图像分类数据集ImageNet上精度更高的SE_ResNet50网络替换YOLOv3算法中的主干网络DarkNet53,提取更...为了准确地识别和定位自然环境中接近成熟或已成熟的树上芒果,提出了一种基于改进YOLOv3的目标检测方法(ISD-YOLOv3)。该方法首先利用在图像分类数据集ImageNet上精度更高的SE_ResNet50网络替换YOLOv3算法中的主干网络DarkNet53,提取更多的芒果特征信息,增强对小目标的识别;其次为有效减少深度残差卷积层在提取特征过程中造成的重要特征信息丢失,借鉴密集网络和VoVNetV2网络,将SE_ResNet50网络中最后3个由残差模块和SE模块构成的SE_ResNet模块改为密集模块、eSE模块及残差连接,实现深层网络中芒果的多层特征信息复用与融合,提高目标检测精度和速度;最后采用自制的树上芒果图像数据集对ISD-YOLOv3模型进行训练与测试,并与原始的YOLOv3_DarkNet53、YOLOv3_SE_ResNet50、Faster R-CNN3种模型进行对比试验。试验结果表明:当输入图像分辨率为608×608像素,交并比(intersection over union,IoU)阈值为0.7时,提出的ISD-YOLOv3方法,在芒果图像测试集上平均精度为94.91%,检测速度达到85帧·s-1(frames per second,FPS);YOLOv3_DarkNet53、YOLOv3_SE_ResNet50、Faster R-CNN这3种方法的平均精度分别为86.03%、91.95%和94.51%,检测速度分别为78FPS、78FPS和6FPS;与其他3种方法相比,ISD-YOLOv3算法检测效果明显更高效,其平均精度分别高出8.88%、2.96%、0.4%,检测速度分别高出7FPS、7FPS、79FPS。表明该方法对自然环境下的树上芒果识别与定位具有更高的检测性能,为实现芒果果实的机器采摘提供了参考。展开更多
文摘Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops.Manually identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations.An atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural production.This paper aims to identify pests in mango trees such as hoppers,mealybugs,inflorescence midges,fruitflies,and stem borers.Because of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing images.The community-based cumulative algorithm was used to classify the pests in the existing system.The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agricul-ture.The Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classification accuracy.Support Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are interested.They are created as suitable classifiers to categorize any dataset in Big Data effectively.The proposed Entropy-ELM-WOA is more capable compared to the existing systems.
文摘The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on their early identification and accurate classification.The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees.Our study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf Blight.To maintain image quality and consistency,pre-processing techniques were employed.We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset.The customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification accuracy.This result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease diagnosis.Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models.Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees.These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.
文摘This study was conducted in the Korhogo region in an age gradient of mango orchards. The goal was to assess diversity and determine the influence of the environment on the composition of these organisms. Sampling was done using pitfall traps, threshing and manual collection. A total of 82 ant species were sampled on all plots. The cultivated plots are richer in ant species than the natural environment. Of the three (3) sampling methods, the Manual Collection proved to be more efficient (eff = 79.52%), followed by threshing (eff = 75.15%) and finally the least effective trap pit (eff = 73.39%). The number of individuals of the species varies according to the environment. The highest value was obtained in the young plot (485 individuals), followed by the intermediate plot (478 individuals), then the older plot (426 individuals) and finally the control plot (320 individuals). The species Camponotus acvapimensis was harvested mainly with 159 individuals. On the other hand, the species Lepisiota sp.1;Camponotus rufigenis, Camponotus sericeus and Oecophylla longinoda were specifically harvested in the control, young, intermediate and aged plots, respectively.
文摘为了准确地识别和定位自然环境中接近成熟或已成熟的树上芒果,提出了一种基于改进YOLOv3的目标检测方法(ISD-YOLOv3)。该方法首先利用在图像分类数据集ImageNet上精度更高的SE_ResNet50网络替换YOLOv3算法中的主干网络DarkNet53,提取更多的芒果特征信息,增强对小目标的识别;其次为有效减少深度残差卷积层在提取特征过程中造成的重要特征信息丢失,借鉴密集网络和VoVNetV2网络,将SE_ResNet50网络中最后3个由残差模块和SE模块构成的SE_ResNet模块改为密集模块、eSE模块及残差连接,实现深层网络中芒果的多层特征信息复用与融合,提高目标检测精度和速度;最后采用自制的树上芒果图像数据集对ISD-YOLOv3模型进行训练与测试,并与原始的YOLOv3_DarkNet53、YOLOv3_SE_ResNet50、Faster R-CNN3种模型进行对比试验。试验结果表明:当输入图像分辨率为608×608像素,交并比(intersection over union,IoU)阈值为0.7时,提出的ISD-YOLOv3方法,在芒果图像测试集上平均精度为94.91%,检测速度达到85帧·s-1(frames per second,FPS);YOLOv3_DarkNet53、YOLOv3_SE_ResNet50、Faster R-CNN这3种方法的平均精度分别为86.03%、91.95%和94.51%,检测速度分别为78FPS、78FPS和6FPS;与其他3种方法相比,ISD-YOLOv3算法检测效果明显更高效,其平均精度分别高出8.88%、2.96%、0.4%,检测速度分别高出7FPS、7FPS、79FPS。表明该方法对自然环境下的树上芒果识别与定位具有更高的检测性能,为实现芒果果实的机器采摘提供了参考。
