Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network

Abstract

Early and accurate diagnosis of plant diseases is a vital step in the crop protection system. In traditional practices, identification is performed either by visual observation or by testing in laboratory. The visual observation requires expertise and it may vary subject to an individual which may lead to an error while the laboratory test is time consuming and may not be able to provide the results in time. To overcome these issues, image based machine learning approach to detect and classify plant diseases has been presented in literature. We have focused specifically on rice plant (Oryza sativa) disease in this paper. The images of the diseased symptoms in leaves and stems have been captured from the rice field. We have collected a total of 619 rice plant diseased images from the real field condition belong to four classes: (a) Rice Blast (RB), (b) Bacterial Leaf Blight (BLB), (c) Sheat Blight (SB) and (d) Healthy Leave (HL). We have used a pre-trained deep convolutional neural network (CNN) as a feature extractor and Support Vector Machine (SVM) as a classifier. We have obtained encouraging results. The early identification of rice diseases by this approach could be used as a preventive measure well as an early warning system. Further, it could be extended to develop a rice plant disease identification system on real agriculture field. 1.

An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

“…In literature, many methods and techniques have been applied to identify the paddy diseases based on its images. The high ratio of these techniques are using generalize image processing techniques [34], Self-Organizing Map (SOM) neural network [6], Gaussian Naïve Bayes [7], Support Vector Machine (SVM) [8], Radial Basis Function Network [9], Deep Neural Network (DNN) based on Jaya Optimization (JO) Algorithm [10], and the recent technique is Convolutional Neural Network (CNN) [11] [12].…”

“…For that, various types of image processing and machine learning techniques have been proposed by previous researchers to detect and classify paddy disease through affected paddy leaf [9][10] [11] [12]. Some of the classification techniques used are Self Organizing Map (SOM) neural network [6], Gaussian Naïve Bayes [7], Support Vector Machine [8], Radial Basis Function Network [9], Optimized Deep Neural Network with Jaya Optimization Algorithm [10] and the recent technique is Convolutional Neural Network (CNN) [11] [12]. Convolutional Neural Network is one of the deep learning techniques that have exposed great promise result in image classification.…”

“…This is due to the ability of CNN to provide end-to-end learning and to work on raw image without requiring a prior knowledge which can reduce the loss of information. However, in [11] CNN was utilized only as feature extractor where the last layer of CNN was eliminated. The extracted features from previous CNN layers were then fed to SVM to classify the dataset.…”

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

“…Early detection of viruses in this way can be used as a precautionary measure as a precautionary measure. In addition, rice production can be increased by establishing special treatment on crop yields [15].…”

Decision Tree-based Machine Learning Algorithms to Classify Rice Plant Diseases

“…It can happen in both tropical and calm conditions, especially in flooded and rainfed marsh territories. Specifically, the illness spread at his best at lower temperatures between 20 and 30 degrees, with relative stickiness above 70% [6]. It is normally seen when solid breezes and nonstop overwhelming downpours happen, permitting the ailment making microscopic organisms effectively spread through seepage beads on injuries of tainted plants.…”