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

TL;DR

This paper proposes an efficient deep learning approach for accurately recognizing agricultural pests in the wild, utilizing transfer learning, attention mechanisms, and custom architectures to improve identification accuracy and robustness.

Contribution

It introduces a novel combination of transfer learning, attention mechanisms, and custom architectures for pest recognition, evaluated on the IP102 dataset.

Findings

High accuracy achieved on IP102 dataset

Robustness demonstrated on additional dataset D0

Effective use of transfer learning and attention mechanisms

Abstract

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.