Institute of Agricultural Information Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai, China.
PLoS One. 2023 Oct 5;18(10):e0286732. doi: 10.1371/journal.pone.0286732. eCollection 2023.
It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper.
准确识别害虫种类并进行有效防治，以减少农产品损失具有重要意义。本项目的研究成果将为防治害虫的传播和减少农产品损失提供理论依据，对提高农产品质量和增加农产品产量具有重要的现实意义。同时，为农民提供了一种有效的防治措施，以确保作物的安全和健康。由于人工识别速度慢、成本高，因此需要建立一套自动害虫识别系统。传统的基于图像的昆虫分类器主要通过机器视觉技术实现，但由于其复杂性高，分类效率低，难以满足应用需求。因此，有必要开发一种新的自动昆虫识别系统，以提高昆虫分类的准确性。昆虫种类繁多，形态各异，田间生活环境复杂。物种之间的形态相似性高，给昆虫分类带来了困难。近年来，随着深度学习技术的飞速发展，利用人工神经网络对昆虫进行分类是建立快速准确分类模型的重要方法。在这项工作中，我们提出了一种基于卷积神经网络的新型模型（MSSN），该模型包括注意力机制、特征金字塔和细粒度模型。该模型具有良好的可扩展性，可以更好地捕捉图像中的语义信息，实现更准确的分类。我们在一个常见的数据集中评估了我们的方法：大规模害虫数据集、PlantVillage 基准数据集，并使用多种评估指标评估模型性能，即宏观平均准确率（MPre）、宏观平均召回率（MRec）、宏观平均 F1 分数（MF1）、准确率（Acc）和几何平均值（GM）。实验结果表明，所提出的算法比现有算法具有更好的性能和通用性。例如，在数据集中，我们获得的最大准确率为 86.35%，超过了相应的技术水平。我们对实验本身进行了消融实验，在各种性能指标下，完整 MSSN（scale 1+2+3）的综合评价是最好的，证明了本文创新方法的可行性。
