Abstract
In the field of agriculture, especially paddy plants, there is a demand for research to classify the paddy diseases at early stages. This is feasible if there are automated systems that can assist the farmers to recognize the paddy diseases from the paddy leaf images of the plants. The recognition of agricultural plant diseases by utilizing the image-processing and machine learning techniques can certainly minimize the reliance on the farmers to protect the yield of paddy crops. In this paper, an attempt has been made to pre-process the images to prepare the feature-set for Classifiers and then feature extraction algorithms are used to extract the relevant features from the processed images. The feature-set is then supplied to the classifiers for identification of Paddy Leaf diseases. The usage of cascaded classifiers has been explored to detect the diseases of paddy leaves. An attempt has also been made to use genetic algorithm with nearest neighbour algorithm to identify the diseases of paddy leaves. The proposed automated system can be used on Android , Windows platform and Apple platform for quickly identifying the paddy leaf diseases as the entire implementation has been performed using MATLAB. The proposed automated system can certainly help the farmers to classify the diseased paddy leaves at early stage to protect the crops from further damage.
Highlights
The machine learning based automated systems are the need of the hour for the Indian fields that produce great amount of rice to identify the diseases as soon as initial symptoms of the diseases appear on the paddy leaves and to save the yield from further damage (Qing & Zexin, 2009)
The proposed automated system can be used on Android, Windows platform and Apple platform for quickly identifying the paddy leaf diseases as the entire implementation has been performed using MATLAB
The proposed automated system can certainly help the farmers to classify the diseased paddy leaves at early stage to protect the crops from further damage
Summary
The machine learning based automated systems are the need of the hour for the Indian fields that produce great amount of rice to identify the diseases as soon as initial symptoms of the diseases appear on the paddy leaves and to save the yield from further damage (Qing & Zexin, 2009). The images of diseased plants are captured and processed, so that the features from the images can be extracted for further processing. Leaf blight, brown blot, sheath blight, and leaf scorch (Rice Production , 2015) These diseases will place severe economic pressure on the rice farmers across the board. When a disease appears on a plant, farmers have to monitor its spread (Basavaraj & Surendra 2020) This disease detection process needs some due diligence during the selection of pesticides. Seemingly-infected cattle and letting themselves be captured by an automated device may be a potential alternative for farmers With such mechanism the farmers can be kept updated on diseases immediately, many of them can save money and time from the major economic losses (Usha & Priyadharshini, 2019)
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