Automated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models

TL;DR

This paper evaluates advanced CNN models for automating pumpkin leaf disease detection, demonstrating that DenseNet-121 achieves 86% accuracy and highlighting the potential of deep learning in agricultural disease diagnosis.

Contribution

The study provides a comprehensive comparison of CNN architectures for pumpkin disease classification, identifying DenseNet-121 as the most effective model in terms of accuracy and efficiency.

Findings

DenseNet-121 achieved 86% overall accuracy.

ResNet-34, DenseNet-121, and EfficientNet were top performers.

CNN models can effectively automate pumpkin disease diagnosis.

Abstract

Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significant losses in yield and quality. Traditional methods of disease identification rely heavily on subjective judgment by farmers or experts, which can lead to inefficiencies and missed opportunities for intervention. Recent advancements in machine learning and deep learning offer promising solutions for automating and improving the accuracy of plant disease detection. This paper presents a comprehensive analysis of state-of-the-art Convolutional Neural Network (CNN) models for classifying diseases in pumpkin plant leaves. Using a publicly available dataset of 2000 highresolution images, we evaluate the performance of several CNN architectures, including ResNet, DenseNet, and EfficientNet, in recognizing five classes: healthy leaves and four common diseases downy mildew, powdery mildew, mosaic disease, and bacterial leaf spot. We fine-tuned these pretrained models and conducted hyperparameter optimization experiments. ResNet-34, DenseNet-121, and EfficientNet-B7 were identified as top-performing models, each excelling in different classes of leaf diseases. Our analysis revealed DenseNet-121 as the optimal model when considering both accuracy and computational complexity achieving an overall accuracy of 86%. This study underscores the potential of CNNs in automating disease diagnosis for pumpkin plants, offering valuable insights that can contribute to enhancing agricultural productivity and minimizing economic losses.