Deep neural network for multi‐class classification of medicinal plant leaves

Abstract

AbstractPlant diseases are a critical issue in the farming industry, and early identification is essential for plant monitoring. The leaves of plants represent the majority of disease symptoms, however, leaf analysis by specialists in laboratories is expensive and time‐consuming. Hence, there is a necessity for automated and more accurate plant disease detection techniques which can help in diagnosing early symptoms to turn down the economic loss. A deep‐learning‐based technique for detecting and classifying plant diseases from leaf images is provided in this research. A diversified image dataset of plant leaves with 12 distinct crops in 22 different categories was used for this work. Several intra‐class and inter‐class variations in the training dataset make it more complex and challenging to train a deep‐learning model. An exhaustive analysis of different deep neural networks has been done with different combinations of optimizers and learning rates. Additionally, five‐fold cross‐validation and testing on separate test images have been done for a detailed investigation of the trained model in different statistical parameters. The proposed approach extended the results to an average cross‐validation accuracy of 98.68%, and average test accuracy of 97.69% is obtained on unseen images having intra‐class and inter‐class variations.