Optimized Deep Learning for Potato Blight Detection Using the Waterwheel Plant Algorithm and Sine Cosine Algorithm

Abstract

Potato blight, sometimes referred to as late blight, is a deadly disease that affects Solanaceae plants, including potato. The oomycete Phytophthora infestans is causal agent, and it may seriously damage potato crops, lowering yields and causing financial losses. To ensure food security and reduce economic losses in agriculture, potato diseases must be identified. The approach we have proposed in our study may provide a reliable and efficient solution to improve potato late blight classification accuracy. For this purpose, we used the ResNet-50, GoogLeNet, AlexNet, and VGG19Net pre-trained models. We used the AlexNet model for feature extraction, which produced the best results. After extraction, we selected features using ten optimization algorithms in their binary format. The Binary Waterwheel Plant Algorithm Sine Cosine (WWPASC) achieved the best results amongst the ten algorithms, and we performed statistical analysis on the selected features. Five machine learning models—Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN)—were used to train the chosen features. The most accurate model was the MLP model. The hyperparameters of the MLP model were optimized using the Waterwheel Plant Algorithm Sine Cosine (WWPASC). The results indicate that the suggested methodology (WWPASC-MLP) outperforms four other optimization techniques, with a classification accuracy of 99.5%.