Feature Extraction and Classification of Plant Leaf Diseases Using Deep Learning Techniques  

作　　者：K.Anitha S.Srinivasan 

机构地区：[1]Department of Electronics and Communication Engineering,Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences(SIMATS),Saveetha University,Thandalam,Chennai,602105,India [2]Instituteof Bio-Medical Engineering,Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences(SIMATS),Saveetha University,Thandalam,Chennai,602105,India

出　　处：《Computers, Materials & Continua》2022年第10期233-247,共15页计算机、材料和连续体（英文）

摘　　要：In India’s economy, agriculture has been the most significantcontributor. Despite the fact that agriculture’s contribution is decreasing asthe world’s population grows, it continues to be the most important sourceof employment with a little margin of difference. As a result, there is apressing need to pick up the pace in order to achieve competitive, productive,diverse, and long-term agriculture. Plant disease misinterpretations can resultin the incorrect application of pesticides, causing crop harm. As a result,early detection of infections is critical as well as cost-effective for farmers.To diagnose the disease at an earlier stage, appropriate segmentation of thediseased component from the leaf in an accurate manner is critical. However,due to the existence of noise in the digitally captured image, as well asvariations in backdrop, shape, and brightness in sick photographs, effectiverecognition has become a difficult task. Leaf smut, Bacterial blight andBrown spot diseases are segmented and classified using diseased Apple (20),Cercospora (60), Rice (100), Grape (140), and wheat (180) leaf photos in thesuggested work. In addition, a superior segmentation technique for the ROIfrom sick leaves with living backdrop is presented here. Textural features of thesegmented ROI, such as 1st and 2nd order WPCA Features, are discoveredafter segmentation. This comprises 1st order textural features like kurtosis,skewness, mean and variance as well as 2nd procedure textural features likesmoothness, energy, correlation, homogeneity, contrast, and entropy. Finally,the segmented region of interest’s textural features is fed into four differentclassifiers, with the Enhanced Deep Convolutional Neural Network provingto be the most precise, with a 96.1% accuracy.

关 键 词：Convolutional neural network wavelet based pca features leaf disease detection agriculture disease remedies bat algorithm 

分 类 号：S43[农业科学—农业昆虫与害虫防治]