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.

农民在农产品产量期间面临的最大挑战之一是与昆虫害虫的斗争。为了解决这个问题并避免经济损失，需要及时采取预防措施，而这需要以简单而有效的方式鉴别昆虫害虫。本文对主要基于名为IP102的健壮数据集的迁移学习与微调，注意力机制和自定义架构等不同方法进行了广泛的实验，并展示了另一个数据集D0的示例来展示我们实验技术的健壮性。

一种高效基于深度学习的野外农业害虫识别方法