Deep Learning Approaches for Potato Leaf Disease Detection: Evaluating the Efficacy of Convolutional Neural Network Architectures
Erlin Erlin, Indra Fuadi, Ramalia Noratama Putri, Dewi Nasien, Gusrianty, Dwi Oktarina
Abstract
In agriculture, timely and accurate detection of plant diseases is essential to obtain healthy crop yields and ensure food security.However, detecting diseases in potato leaves is challenging because of the complex symptoms and variability in leaf appearances.This requires the development of an effective and efficient method that can overcome these challenges and improve disease detection accuracy.Utilizing the power of computer vision and deep learning, this paper presents a comprehensive study on potato leaf disease detection using a multi-architecture Convolutional Neural Networks (CNNs) approach.We evaluate five different CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, and AlexNet, to assess their classification capabilities.The research encompassed the dataset collection, data augmentation, model selection, hyperparameter tuning, and evaluation, leading to a rigorous analysis of detection accuracy, model convergence, and training efficiency.Our findings revealed that ResNet50 was the standout performer, achieving a remarkable 97% testing accuracy and 98% specificity.Conversely, the VGG19 architecture was the least effective.A consistent challenge across all models was accurately classifying categories of healthy leaves, indicating a potential area for model refinement.This study not only highlights the efficacy of deep learning in plant health diagnosis but also highlights the importance of specificity as an important metric in such tasks.The results of our study provide a promising avenue for real-time diagnosis of potato leaf diseases in the field, paving the way for healthier crops and increased agricultural productivity.