A Robust Restructured LeNet (RR LeNet) for Plant Leaf Disease Recognition
Keywords:
Convolutional Neural Networks, Deep Learning, Plant Disease Recognition, Object Categorization, Object Detection.Abstract
Convolution Neural Networks (CNN) with Deep learning has attained remarkable achievement in categorizing major diseases in plants. The objective of this study is to identify diseases in Tomato, Corn and Apple plants using CNN. There are several popular Convolutional Neural Networks for object detection and object categorization from leaf images. In this work, Robust Restructured LeNet architecture is applied to the Plant Village data set. It has been exhibited that neural networks could detect the colors as well as the quality of lesions attributed to corresponding ailments that are similar to man-made confirmatory diagnosis. The database contains 18789 imageries. Out of these images, 20% of the imageries were kept apart for testing and 80% imageries were utilized for training. A maximum validation accuracy of 96.64%, 96.23%, and 96.85% was achieved over 30 epochs of testing, whereas a maximum of 98.46%, 96.79%, and 97.28% of accuracy were obtained over 30 epochs while training for tomato, corn, and apple respectively.
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