Paddy Leaf diseases identification on Infrared Images based on Convolutional Neural Networks
This work addresses plant disease identification for paddy farmers, but it is incremental as it applies an existing deep learning method to a new type of data (infrared images).
The paper tackled the problem of identifying paddy leaf diseases using infrared images to help farmers mitigate losses, achieving an accuracy of 88.28% on a dataset with five disease classes and one healthy class.
Agriculture is the mainstay of human society because it is an essential need for every organism. Paddy cultivation is very significant so far as humans are concerned, largely in the Asian continent, and it is one of the staple foods. However, plant diseases in agriculture lead to depletion in productivity. Plant diseases are generally caused by pests, insects, and pathogens that decrease productivity to a large scale if not controlled within a particular time. Eventually, one cannot see an increase in paddy yield. Accurate and timely identification of plant diseases can help farmers mitigate losses due to pests and diseases. Recently, deep learning techniques have been used to identify paddy diseases and overcome these problems. This paper implements a convolutional neural network (CNN) based on a model and tests a public dataset consisting of 636 infrared image samples with five paddy disease classes and one healthy class. The proposed model proficiently identified and classified paddy diseases of five different types and achieved an accuracy of 88.28%