2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 1 , PP: 27-38, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
R. Kiruthika 1 * , B. Arun kumar 2
Doi: https://doi.org/10.54216/JISIoT.170103
In these modern agriculture system crop pests causes major social, economic and environmental issues worldwide. Each pest necessitates an alternative method of control and precise detection has become a very important challenge in agriculture. Deep learning technique shows remarkable results in image identification. Standard pest detection framework might struggle with accuracy due to complicated algorithms and lack of data, and result in incorrect detection, which leads to harm the crop environment. To end this, we developed a novel framework named Transformer and ResNet Improved Pest Classification and Identification Detection (TRIP-CID) for crop pest classification and identification. At first, the pest images are obtained through the benchmark dataset for pre-processing. The Pre-processed images are immediately delivered to the Improved ResNet (IR-Net) and Pyramidal Vision Transformer (PVT) for multi-scale spatial, channel and contextual feature maps extraction within three stages. The extraction feature maps in the two modules are combined to produce a superior feature map. Then refined feature maps was fed to the three distinct Machine Learning (ML) classifiers offered pest detection outcomes. For accurate results, we employ ensemble-voting technique, which outputs effective pest detection result that is vastly used for particle suggestion. Finally, we utilized presented technique for detecting and identify crop pest in 10-pest class for instance larva of laspeyresia pomonella, Euproctis pseudoconspersa strand, Locusta migratoria, acrida cinerea, empoasca flavescens, spodoptera exigue, parasa lepida, chrysochus chinensi, L.pomonella types of insects pests and larva of S. exigua. Additionally, the suggested methodology has shown to provide experts and farmers with quick, efficient assistance in identifying pests, saving money and preventing losses in agricultural output.
Deep learning , Pest detection framework , TRIP-CID , Ensemble voting technique , Feature extraction
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