Year: 2025
Type: article
Abstract: This study presents an automated approach for the detection and classification of coastal area arecanut diseases using Dual Convolutional Neural Networks (DCNNs). A custom dataset consisting of 1,000 images, captured under expert supervision from Navilgon village in Honnavara taluk, Karnataka. This was developed to represent four classes like Healthy, Rot, Split, and Rot+Split. All these images were preprocessed by resizing them to 128×128 pixels and converted into numerical arrays to facilitate model training. The proposed DCNN model incorporates double convolutional blocks, batch normalization, dropout, and max-pooling layers to efficiently extract and learn both lower-level and upper-level features. The architecture culminates in a softmax-activated dense layer to classify the input images into one of the four disease classes. The dataset was split in an 80:20 ratio for training and testing, and the model was trained using the ReLU activation function, Adam optimizer, and categorical cross-entropy as the loss function. Confusion matrix shows Healthy and Rot classes have the highest accuracy of 80%. Similarly Split and Rot + Split are slightly lower accuracy of 65%. Key Words: arecanut, agriculture, healthy, diseases,entropy
DOI: 10.55041/ijsrem51073
Authors: Dept of Computer Science and Engg., KVGCE, Sullia, Dinesh G Hegde
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4411839901