Pest Detection on Green Mustard Plants Using Convolutional Neural Network Algorithm - Arifin, Nurhikma; Rachmini, Siti Aulia; Rusman, Juprianus (Indonesian Journal of Artificial Intelligence and Data Mining, 2025)
The productivity of mustard greens is vulnerable to pests and diseases that can threaten the yield and quality of the harvest. This study aims to detect pests on green mustard plants using the Convolutional Neural Network (CNN) method. The dataset used in this research consists of 450 images, with 225 images of pest-infested mustard greens and 225 images of healthy mustard greens. These 450 datasets are divided into 400 training data and 50 testing data. The testing was conducted fifteen times using CNN architectures with 2, 3 and 4 convolutional layers, having filter numbers of (64,32) (64, 32, 16) and (64, 32, 16, 8) respectively, and learning rates ranging from 0.1 to 0.00001 with the Adam optimizer. Based on the testing results of the learning rate and the number of layers, it was found that a learning rate of 0.001 provided the best performance with the highest accuracy and the lowest loss, especially in the model with 3 layers (64, 32, 16), which achieved an accuracy of 94% and a loss of 24.92%. A learning rate that is too high (0.1) or too low (0.00001) results in poor performance and instability, with low accuracy and high loss. Therefore, selecting the appropriate learning rate is crucial to achieving optimal results in model training.