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Potatoes are the third-largest food crop globally, but their productionfrequently encounters difficulties because of aggressive pest infestations. Theaim of this study is to investigate the various types and characteristics ofthese pests and propose an efficient PotatoPestNet AI-based automatic potatopest identification system. To accomplish this, we curated a reliable datasetconsisting of eight types of potato pests. We leveraged the power of transferlearning by employing five customized, pre-trained transfer learning models:CMobileNetV2, CNASLargeNet, CXception, CDenseNet201, and CInceptionV3, inproposing a robust PotatoPestNet model to accurately classify potato pests. Toimprove the models' performance, we applied various augmentation techniques,incorporated a global average pooling layer, and implemented properregularization methods. To further enhance the performance of the models, weutilized random search (RS) optimization for hyperparameter tuning. Thisoptimization technique played a significant role in fine-tuning the models andachieving improved performance. We evaluated the models both visually andquantitatively, utilizing different evaluation metrics. The robustness of themodels in handling imbalanced datasets was assessed using the ReceiverOperating Characteristic (ROC) curve. Among the models, the Customized TunedInception V3 (CTInceptionV3) model, optimized through random search,demonstrated outstanding performance. It achieved the highest accuracy (91%),precision (91%), recall (91%), and F1-score (91%), showcasing its superiorability to accurately identify and classify potato pests.

PotatoPestNet: A CTInceptionV3-RS-Based Neural Network for Accurate Identification of Potato Pests