Rice serves as the primary food source for nearly half of the global population, with Asia accounting for approximately 90% of rice production worldwide. However, rice farming faces significant losses due to pest attacks. To prevent pest infestations, applying appropriate pesticides specific to the type of pest in the field is crucial. Traditionally, pest identification and counting have been performed manually using sticky light traps, but this process is time-consuming. This study developed a machine vision system using a dataset of 7328 high-density images (1229 pixels per centimetre) of planthoppers collected in the field using sticky light traps. The dataset included four planthopper classes: brown planthopper (BPH), green leafhopper (GLH), white-backed planthopper (WBPH), and zigzag leafhopper (ZIGZAG). Five deep CNN models—ResNet-50, ResNet-101, ResNet-152, VGG-16, and VGG-19—were applied and tuned to classify the planthopper species. The experimental results indicated that the ResNet-50 model performed the best overall, achieving average values of 97.28% for accuracy, 92.05% for precision, 94.47% for recall, and 93.07% for the F1-score. In conclusion, this study successfully classified planthopper classes with excellent performance by utilizing deep CNN architectures on a high-density image dataset. This capability can potentially serve as a tool for classifying and counting planthopper samples collected using light traps.

Figure 1: Machine vision system used for image acquisition

Figure 2: Sample of light trap inside the black box

Ibrahim MF, Khairunniza-Bejo S, Hanafi M, Jahari M, Ahmad Saad FS, Mhd Bookeri MA. Deep CNN-Based Planthopper Classification Using a High-Density Image Dataset. Agriculture. 2023; 13(6):1155.

Artikel penuh: https://doi.org/10.3390/agriculture13061155

Tarikh Input: 31/07/2023 | Kemaskini: 31/07/2023 | ainzubaidah