Potato Leaf Disease Detection Using Deep Learning: Development of a Novel Model & Comparative Analysis with Existing Architectures

Abstract

Potato is one of the most crucial food security crops to the world and its production in Bangladesh is a great input to the sustenance of the country as well as being a vital food source to the people. However, its commercial cultivation is facing the imminent danger of a number of leaf diseases, including the early blight, late blight, viral diseases and insect damage. The diseases mentioned severely kill the yield and quality and the diagnosis should be made early and accurate. Conventional manual detection systems are prone to flaws and show a slow response rate, hence there is a dire need to have an automated system that is scalable. This paper introduces a deep learning framework used to detect and label five different conditions of potato leaves, which includes Early Blight, Late Blight, Virus, Insect, and Healthy. A custom Convolutional Neural Network (CNN) was created, and it was trained on more than one thousand primary images. The system has been compared to four CNNs comprising VGG19, ResNet50, MobileNetV2 and InceptionV3 based on standard performance measures like accuracy, precision, recall and F1-score. This model had a classification accuracy of 98.95% which makes it have a great potential in precision agriculture. Knowledge of uncertainties in terms of accuracy was ensured along with the development of visual interpretation techniques that increase the interpretability and trust on the decisions made by the model. This study not only supports the power of deep learn