COMPACT CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR ONION DISEASE CLASSIFICATION USING CROP IMAGES

Detecting disorders in crops at an initial phase is important for improved agricultural productivity. Different diseases like purple blotch in onions affect crop quality worldwide. Traditional approaches for identifying purple blotch require time, wide examination, and frequent farm observation. With technical improvements in recent years, agriculturalists have been able to discover optimal solutions that have caused higher harvests. This article presents compact Convolutional Neural Network (CNN) architecture for onion disease (purple blotch) classification from crop images. This Onion Crop Disease Dataset (OCDD) contains 1000 images of healthy and infected crops. Four distinct architectures InceptionV3, Xception, EfficientNetB7, and DenseNet201 are compared. Subsequent trials for assessment are applied. DenseNet201 delivers 94.54% accuracy in comparison to other models.