Plant Stress Classification for Smart Agriculture utilizing Convolutional Neural Network - Support Vector Machine

Abstract

Plant stresses considerably increasing due to changing environmental conditions. This study aims to classify plant stresses using a hybrid convolutional neural network and support vector machine. This study used soybean leaf images with identified plant stresses in model training, testing, and validation activities. The results show that the hybrid model achieves an overall accuracy of 98.02%. This study found that the model is suitable for plant stress classification. This work contributes by providing a hybrid model that can potentially perform in a smart agriculture environment. This study presents some studies to extend their contribution.