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One of the biggest challenges that the farmers go through is to fight insectpests during agricultural product yields. The problem can be solved easily andavoid economic losses by taking timely preventive measures. This requiresidentifying insect pests in an easy and effective manner. Most of the insectspecies have similarities between them. Without proper help from theagriculturist academician it is very challenging for the farmers to identifythe crop pests accurately. To address this issue we have done extensiveexperiments considering different methods to find out the best method amongall. This paper presents a detailed overview of the experiments done on mainlya robust dataset named IP102 including transfer learning with finetuning,attention mechanism and custom architecture. Some example from another datasetD0 is also shown to show robustness of our experimented techniques.

An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild