Detection of Jackfruit Leaf Disease Using Machine Learning and Deep Learning

Abstract

The detection of diseases in agricultural crops plays a critical role in maintaining healthy yields. Jackfruit (Artocarpus heterophyllus), a tropical fruit, is susceptible to various diseases that impact its leaves. Timely disease detection can significantly reduce crop loss and improve the quality of the harvest. This paper proposes a system for detecting jackfruit leaf diseases using machine learning (ML) and deep learning (DL) techniques. A dataset of healthy and diseased jackfruit leaf images is used to train both traditional ML algorithms (Random Forest) and DL models (Convolutional Neural Networks). The results indicate that deep learning models, particularly CNNs, outperform traditional ML models in terms of classification accuracy, precision, and recall. This system serves as an effective tool for early detection and management of jackfruit leaf diseases, offering an automated solution for farmers.

Introduction

1. Introduction

Jackfruit, a widely grown tropical fruit, is prone to diseases like leaf spot, rust, and powdery mildew. Manual disease detection is time-consuming and requires expertise. Therefore, the study aims to develop an automated image-based detection system using ML and DL to assist farmers with early disease identification and efficient crop monitoring.

2. Objective

• Compare the performance of traditional Machine Learning (e.g., Random Forest) and Deep Learning models (e.g., CNNs).

• Detect and classify diseases from images of jackfruit leaves.

• Enable early intervention through automated monitoring.

3. Literature Review Highlights

• CNNs are widely used for image-based disease detection due to their ability to learn features (texture, shape, color) from raw images.

• Image preprocessing and data augmentation (rotation, flipping) improve model performance.

• CNN models effectively classify multiple disease types, such as anthracnose and bacterial leaf blight.

• Hyperspectral imaging shows potential for early disease detection by identifying biochemical changes invisible to the naked eye.

• Traditional ML (e.g., Random Forest, SVM) can achieve good accuracy with proper feature extraction (GLCM, LBP) and is more computationally efficient.

• Ensemble learning (e.g., combining Random Forest, SVM, Neural Networks) improves accuracy and robustness.

• Novel applications like sentiment analysis techniques from NLP were also explored for cross-domain adaptation.

4. Methodology

A. Dataset & Preprocessing

• Collected jackfruit leaf images and resized them to 224x224 pixels.

• Normalization: Pixel values scaled to [0, 1].

• Data Augmentation: Used to increase dataset diversity and reduce overfitting.

• Dataset split into training, validation, and test sets.

B. Model Architecture

• Simple CNN model built using TensorFlow/Keras:

  • Flatten layer to reshape images.

  • Dense Layer with 128 neurons (ReLU activation).

  • Output Layer with 4 neurons (Softmax activation) for multi-class classification: Healthy, Leaf Spot, Rust, Powdery Mildew.

• Adam Optimizer used for training.

• Categorical cross-entropy used as the loss function.

C. Training

• Model trained for 10 epochs using fit() method.

• Training and validation performance monitored using accuracy and loss curves.

5. Results

• Training Accuracy: ~98%

• Validation Accuracy: ~96%

• Testing Accuracy: ~94%

• High accuracy confirms good generalization ability.

• The model successfully predicted the correct disease class (e.g., "Red Rust") on test images.

Conclusion

This research demonstrates the power of deep learning, particularly Convolutional Neural Networks (CNNs), in the detection and classification of diseases in jackfruit leaves. The model built using TensorFlow and Keras achieved high accuracy, outperforming traditional machine learning approaches. The results indicate that deep learning models can significantly enhance agricultural practices by providing quick and accurate disease detection. Future work could include exploring transfer learning with pre-trained models and expanding the dataset to include more diseases. This model could be deployed on mobile devices to aid farmers in real-time disease detection and management.

References

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Copyright

Copyright © 2025 Mohammad M. Tagare , Urmila R. Pol , Parashuram S. Vadar, Tejashree T. Moharekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.