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A Smart Agronomy: Deep Learning Process for Recognition and Classification Plant Leaf Diseases
Abstract
The main part of the agriculture process is the timely detection of leaf diseases to have a healthy growth. In routine implementation, the identification of diseases is realized either by manual or laboratory testing. Physical testing involves few expertise and results could vary from individuals which can result in false interpretation while the latter requires extra time and might not be able to deliver the production, due to which the spread of disease gradually increases. Hence an automated system is required for the identification and classification of the disease. This chapter intends leaf sickness detection and recognition by applying deep learning for two data split ratios. The classification task is performed using Alex-net, a pre-trained architecture. The data set has three categories of leaf disease, namely, bacterial leaf blight, brown spot, and leaf blast, each consisting of 40 infected images. The proposed architecture classifies the diseases into three categories. The comparison study for various performance metrics—such as recall, precision, and specificity—is measured.
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