Plant Disease Detection and Classification using Convolutional Neural Networks (CNN)

Abstract

Farmers and producers may suffer significant financial losses due to plant diseases. Effective control of plant diseases requires early identification and categorization of the illnesses. However, traditional plant disease detection techniques are frequently labor- and time-intensive. Plant disease detection has found a possible collaborator in deep learning and machine learning. Consequently, a novel deep-learning technique is proposed for classifying and diagnosing plant illnesses. It uses a transfer learning methodology built on a convolutional neural network architecture consisting of 5 convolutional, 2 fully- connected, and 3 max pooling layers. The algorithm has a 98% accuracy rate when tested on a publicly accessible dataset of images of plant diseases. The CNN model was trained with the PlantVillage dataset, which is available on Kaggle. From the results and a comparison with previous works, it is evident that the chosen approach performed well in classifying plant diseases with expected accuracy.