Jackfruit Leaf Disease Classification Using Computer Vision and CNN Models Integrated with a Gradio Web Interface

Jackfruit (Artocarpus heterophyllus) is one of the most important fruit crops in Bangladesh, but its productivity is often reduced due to leaf diseases such as black spot and chewing insect damage. Early and accurate detection of these diseases is crucial to prevent serious yield loss. However, traditional diagnosis mainly depends on expert visual inspection, which is time consuming, subjective, and often inaccessible to farmers in rural areas. To overcome this limitation, we propose a deep learning–based artificial neural system for jackfruit leaf disease detection using a field-collected dataset of approximately 4,500 images categorized into three classes: Healthy, Black Spot, and Chewing Insect Damage. Transfer learning is applied along with image preprocessing, data augmentation, and model tuning using DenseNet201, VGG19, and InceptionResNetV2 under varying real-world field conditions in Bangladesh. The final notebook uses ImageNet-pretrained bases, Adam optimization, categorical cross-entropy loss, and five epochs; the models were evaluated using accuracy and loss curves, confusion matrices, and classification reports. Experimental results show that DenseNet201 achieved the highest test accuracy of 94.52%, followed by VGG19 at 90.00% and InceptionResNetV2 at 87.33%. A Gradio interface allows the user to upload a jackfruit leaf image and receive class probabilities and a final prediction; inputs that do not pass the implemented validation gate are handled as undefined. This deep learning–based disease diagnosis system can support better crop management practices, timely decision-making, and long-term sustainable agriculture. The study is limited by its dataset scope, included classes, and lack of external multi-environment validation.

Jackfruit leaf disease, computer vision, convolutional neural networks, transfer learning, Gradio web interface

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