Abstract
The survival of human beings is generally based on the proper productivity of agriculture. The paddy plant is considered as a major planting crop in improving the economical level of our country. Nowadays, the yield level of paddy crop might be minimized due to several diseases. Bacteria, fungi, virus and certain harmful insects are the main causative agents for such disease occurrence on the paddy crop. The diseases which affect the early stage of the paddy crops influences in the whole stage of crop cultivation. In early days of agriculture, the manual detection of diseases has been carried out by farmers. Image processing is one of the emerging techniques for identifying and classifying the different types of diseases and it overcomes the issues encountered during the manual detection of diseases. Image processing technique solves several issues involved in the cultivation of crops including, recognition and classification of plant diseases, discrimination of certain weeds and disease forecasting.
Highlights
Plant diseases are one of the causes in the reduction of quality and quantity of agriculture crops [1]
We briefly present our approach to solving the problem of automatic detection and classification of rice plant diseases
The research work of this paper addressed the detection of diseases of fruit plant through image processing techniques with the help of optimized machine learning algorithms
Summary
Plant diseases are one of the causes in the reduction of quality and quantity of agriculture crops [1]. This article attempts to apply concepts of Machine Learning and Image Processing to solve the problem of automatic detection and classification of diseases of the rice plant, which is one of the important foods in India. As an automated solution of this problem, cameras can be deployed at certain distances in the farm to capture images periodically. These images can be sent to a central system for analysis of diseases; the system can detect the disease and give information about the disease and pesticide selection [4]. In addition to the environmental factors, the plant with a diseased leaf can be identified using Image processing. Diagnosis will help in taking the necessary actions to increase the produce and reduce failure of crops
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