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Potatoes are the third-largest food crop globally, but their production
frequently encounters difficulties because of aggressive pest infestations. The
aim of this study is to investigate the various types and characteristics of
these pests and propose an efficient PotatoPestNet AI-based automatic potato
pest identification system. To accomplish this, we curated a reliable dataset
consisting of eight types of potato pests. We leveraged the power of transfer
learning by employing five customized, pre-trained transfer learning models:
CMobileNetV2, CNASLargeNet, CXception, CDenseNet201, and CInceptionV3, in
proposing a robust PotatoPestNet model to accurately classify potato pests. To
improve the models' performance, we applied various augmentation techniques,
incorporated a global average pooling layer, and implemented proper
regularization methods. To further enhance the performance of the models, we
utilized random search (RS) optimization for hyperparameter tuning. This
optimization technique played a significant role in fine-tuning the models and
achieving improved performance. We evaluated the models both visually and
quantitatively, utilizing different evaluation metrics. The robustness of the
models in handling imbalanced datasets was assessed using the Receiver
Operating Characteristic (ROC) curve. Among the models, the Customized Tuned
Inception 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 superior
ability to accurately identify and classify potato pests.
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