An Efficient Deep Learning-based approach for Recognizing Agricultural
  Pests in the Wild
Mohtasim Hadi Rafi Mohammad Ratul Mahjabin Md Sabbir Rahman
One of the biggest challenges that the farmers go through is to fight insect
pests during agricultural product yields. The problem can be solved easily and
avoid economic losses by taking timely preventive measures. This requires
identifying insect pests in an easy and effective manner. Most of the insect
species have similarities between them. Without proper help from the
agriculturist academician it is very challenging for the farmers to identify
the crop pests accurately. To address this issue we have done extensive
experiments considering different methods to find out the best method among
all. This paper presents a detailed overview of the experiments done on mainly
a robust dataset named IP102 including transfer learning with finetuning,
attention mechanism and custom architecture. Some example from another dataset
D0 is also shown to show robustness of our experimented techniques.