Chili Leaves Disease Identification Using Artificial Neural Network Algorithms

Deep learning (DL) and machine learning (ML) approaches have made it easier to recognize and identify objects in photos. Following in the footsteps of their success in other industries, neural networks have lately entered a few agricultural and farming applications. Plant disease detection software may help farmers manage their crops more effectively and increase yields. Utilizing images to diagnose plant disease in crops is a difficult task in and of itself. The use of specialist control measures requires both the detection and identification of species. In this chapter, a study of research initiatives that used Google Net, a kind of DL, to solve different plant disease detection difficulties was undertaken. One hundred eighty images of chili illness from the Kaggle public domain were given to the Google-Net convolutional neural network (CNN) in order to assess training accuracy. The final training accuracy ranges from 30% to 100% by taking into consideration various learning characteristics, including CNN optimizers SGDM, ADAM, and RMSProp. Max Epochs and giving Dropout probability, Strides, Dilation factor, and padding values as constants. The Google-Net CNN architecture achieves 98.61% accuracy in SGDM and ADAM with Max Epochs of 20, 30, and 40, respectively, according to the simulation. Moreover, it was claimed that SGDM is suitable for training the Chili Dataset, whose Max Epochs are substantially smaller than in SGDM and ADAM. The two optimizers have three epochs: 20, 30, and 40, with low epoch 20 producing superior accuracy.