PROACTIVE DIAGNOSIS OF MUNG BEAN LEAF DISEASES IN A CONTROLLED ENVIRONMENT USING MACHINE LEARNING TECHNIQUES
DOI:
https://doi.org/10.53555/ephijse.v11i1.310Keywords:
Mung leaf, Machine Learning, SVMAbstract
This paper puts forth a model that classifies Mung (Vigna mungo L.) leaves to check if they are fit as a fiddle or have caught some disease, using a Machine Learning algorithm. The dataset was made in a proper controlled setup, where each data item (image) shows just one leaf against a white background, collected from the South Gujarat side of India. Support Vector Machine (SVM) models were trained for doing this work, focusing on spotting three types of Mung leaf diseases, as well as when a leaf is perfectly healthy. The model nicely pulls out the detailed features related to different diseases. Experiment results show that the SVM gets an identification accuracy of 86.4% on the Mung leaf image dataset. If diseases are caught early, it can help our kisaans in increasing their yield. The main aim was to automatically identify Mung leaf diseases using advanced machine learning techniques and image data.
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