Study on stages of diseases detection in plant using Deep Convolutional Neural Network (CNN) in Agriculture

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Saraswathi. E, et. al.

Abstract

Deep learning nowadays shows a drastic result by applying many techniques such as CNN (Convolutional Neural Networks). Labelling an image in various detecting technique such a object, face recognition, handwriting and so on is a various process to identify the exact partition of efficient results. The experience of agriculture phenomena brings a various impact on increasing the productivity by avoiding erosion, air pollution, pesticide resistance and so on. When plan diseases are fed as image to investigate the classification of affected biotic and abiotic crops from the field. Eventually the essential prediction of automatic and better results of removal of noise by applying chain rule a deep neural chain structure describe the betterment of efficiency by detecting the affected part. Previous works using machine learning and deep learning lacked in visualisation of affected part of leaves in agriculture. By applying convolution Neural Network on image recognition several steps such as segmentation, selection, activation function and feature extraction helps to get accuracy compare to other image processing techniques.

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