Abstract: Agriculture is of utmost importance to the Indian economy. The production of main crops such as rice, maize, tomatoes, and potatoes go a long way to affect the livelihoods of the farmers. However, these crops are highly susceptible to many challenges most especially diseases that attack them; such maladies drastically reduce productivity. Early and rapid identification of such diseases are critical for initiating appropriate measures to contain potential losses. Deep learning techniques will be harnessed in this study involving feature extraction from digitized images of diseased plants for the accurate identification of maladies. Deep learning has also previously proven an efficient tool in handling very large datasets and finding patterns between normal and anomalous leaves. This review looks at different deep learning algorithms like VGG16, VGG19, RegNet50, EfficientNet etc. used in different studies and checks the accuracy, efficiency, and reliability of these models in detecting diseases in crops. The information learned from this review will help to find out the best deep learning algorithms for crop diseases detection. By better identifying and handling diseases, this study aims to increase productive crop farming in India which will help the sustainable growth of the agricultural sector.
Keywords: Crop Disease Detection, Convolutional Neural Networks, Image Classification, Deep Learning, Transfer Learning, Internet of Things.
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DOI:
10.17148/IJARCCE.2025.14719
