Recognition of Cotton Plant Diseases Using Deep Learning Architecture

Abstract

Agriculture is a vital component of any nation’s economy, and India is renowned for being an agro-based economy. One of the main objectives in agriculture is to cultivate robust crops that are free from diseases. Cotton has a crucial role in generating money in India. India is the world’s leading producer of cotton. Premature leaf abscission or the onset of diseases can have detrimental effects on cotton harvests. However, throughout generations, farmers and agricultural experts have consistently faced numerous problems and chronic issues in the realm of planting, including the prevalence of various cotton diseases. There is a pressing demand in the agricultural information sector for a rapid, efficient, cost-effective, and reliable technique to detect cotton infections. This is crucial since severe cotton diseases can result in a complete failure of grain harvest. Deep learning is utilized to address the challenges of image processing and classification due to its exceptional performance. This technique employs the MobileNet paradigm. Based on the experimental results, the model attains a training accuracy of 0.95 and a validation accuracy of 0.98.

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