Palm fruit ripeness classification using BorneoNet for improved accuracy in precision agriculture

This study aims to classify palm fruit ripeness using the proposed Borneo Neural Network (BorneoNet) to improve accuracy over traditional models and provide a practical solution for precision agriculture. A novel dataset of RGB images was collected and categorized into three ripeness stages: ripe, half ripe, and unripe, then divided into 50 percent training, 25 percent validation, and 25 percent testing subsets. The BorneoNet architecture consists of three convolutional layers with batch normalization and max-pooling, followed by two fully connected layers. Its performance was compared with kNN, Naive Bayes, SVM, Random Forest, and XGBoost using accuracy, precision, recall, F1-score, and Kappa metrics, while the McNemar test was used to confirm statistical significance. The findings showed that BorneoNet outperformed all traditional models with an accuracy of 0.8125, a precision of 0.8182, a recall of 0.7899, an F1-score of 0.7848, and a Kappa value of 0.7134, indicating strong agreement with the true labels. Overall, the results suggest that BorneoNet is a reliable and efficient model with lower computational complexity than traditional and deep CNN architectures. The practical implications include its potential use as a lightweight real-time tool for farmers, with future development focusing on dataset expansion and integration into mobile applications to support precision agriculture.