early discovery and classification of plant diseases with convolutional neural networks and nano biosensors

Objective infections caused by viruses and bacteria are the primary microbe-related problems that significantly decrease agricultural productivity worldwide. currently, the identification of pathogens is particularly difficult due to the prevailing living conditions. biosensors are now widely used for the surveillance of microbial and viral particles. materials and methods preventing crop loss and reducing economic and environmental effects requires early plant disease identification. tracking plant infection nanoparticles has made early disease diagnosis possible due to nanotechnology and biosensors. pathogens including bacteria, fungi, and viruses form nanoparticles with unique chemical traces that may be detected by sensitive nano biosensors. precision agriculture now allows faster responses and more specific disease control. deep learning (dl) methods, particularly convolutional neural networks (cnns), can learn hierarchical patterns in nano biosensor data and accurately distinguish healthy and infected plants, even in early infection stages. this expands precision agriculture and disease management. results the study utilizes the ecpd-cnn-nbs model to identify bacterial spot (bs) disease in peach plants by analyzing their leaf images. the model can also be employed for ecpd detection. the experiments conducted in this paper utilize the publicly accessible plantvillage dataset to obtain leaf pictures of peach plants. conclusions the proposed system attains a learning accuracy of 99.55% and a testing accuracy of 99.01% by utilizing 10,013 learning parameters.

early discovery and classification of plant diseases with convolutional neural networks and nano biosensors

objectiveinfections caused by viruses and bacteria are the primary microbe-related problems that significantly decrease agricultural productivity worldwide. currently, the identification of pathogens is particularly difficult due to the prevailing living conditions. biosensors are now widely used for the surveillance of microbial and viral particles. materials and methodspreventing crop loss and reducing economic and environmental effects requires early plant disease identification. tracking plant infection nanoparticles has made early disease diagnosis possible due to nanotechnology and biosensors. pathogens including bacteria, fungi, and viruses form nanoparticles with unique chemical traces that may be detected by sensitive nano biosensors. precision agriculture now allows faster responses and more specific disease control. deep learning (dl) methods, particularly convolutional neural networks (cnns), can learn hierarchical patterns in nano biosensor data and accurately distinguish healthy and infected plants, even in early infection stages. this expands precision agriculture and disease management. resultsthe study utilizes the ecpd-cnn-nbs model to identify bacterial spot (bs) disease in peach plants by analyzing their leaf images. the model can also be employed for ecpd detection. the experiments conducted in this paper utilize the publicly accessible plantvillage dataset to obtain leaf pictures of peach plants. conclusionsthe proposed system attains a learning accuracy of 99.55% and a testing accuracy of 99.01% by utilizing 10,013 learning parameters.