Integrating Deep Learning and mSVM for Category of Class Prediction of Spice Plant Leaf Diseases

E arly disease detection is fundamental to protect the crops and provide early treatment. Plants with medicinal values are rare and need complete care. Black pepper is a spice herb, highly used as medicinal plant. The diseases prominent in black pepper spice are anthracnose, phytophthora, slow wilt, quick wilt, and yellowing. The proposed work suggests deep learning-based prediction and classification. The proposed work utilizes a benchmark dataset created in real time environment. The dataset is preprocessed, segmented, and labeled into classes under expert supervision. Deep neural network ResNet − 50 is trained with novice data. The trained features are saved in an array. This data is matched in linear with extracted feature of Support vector machine SVM, a machine learning algorithm used for disease prediction. This is done by passing and mapping the hyperparameter features of Renet-50 as an array and mapping it to feature array of multiclass support vector machine. Hence there is no requirement of training machine learning separately. This results in fast training and early disease prediction .