Farmland Pest Detection Based on YOLO-V5l and ResNet50

春源 柳
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Abstract

At present, China’s farmland is increasingly affected by insect pests. Insect situation analysis can formulate different plans to control farmland pests according to the insect situation in different regions. Traditional pest situation analysis relies on manual collection and statistics, which is time-consuming and labor-consuming. With the development of deep learning technology in the field of computer vision, this paper proposes to build a farmland pest detection model by combin-ing YOLO-V5l target detection and ResNet50 neural network. Insects have the characteristics of diverse body shapes, missing scales and falling limbs in the image data, which have a great impact on the target detection and classification. Therefore, this paper roughly classifies 28 pests into seven A~G species according to their body shapes and colors, uses YOLO-V5l model to detect and count them, and then substitutes the detection results into ResNet50 recognition model to deter-mine their species. This method greatly reduces the false detection rate of farmland pest detection. Moreover, this paper proposes a predictive enhancement algorithm. After the pest images to be detected are enhanced, they are brought into the recognition model respectively, and the recognition results are weighted average to get the final results. mAP.5:.95 of single YOLO-V5l model was 71.4%, the average accuracy rate was 80.91%, and the missed detection rate was 5.39%. The average accuracy of the pest detection model proposed in this paper is 89.56%, which improves the recognition accuracy of farmland pests. The model will improve the shortcomings of original artificial statistics and promote the development of Intelligent Agriculture in China.
基于YOLO-V5l和ResNet50的农田有害生物检测
目前,中国的农田受到害虫的影响日益严重。病情分析可以根据不同地区的病虫害情况,制定不同的农田病虫害防治方案。传统的病虫害态势分析依赖于人工采集和统计,耗时耗力。随着计算机视觉领域深度学习技术的发展,本文提出将YOLO-V5l目标检测与ResNet50神经网络相结合,构建农田害虫检测模型。昆虫在图像数据中具有形体多样、鳞片缺失、四肢掉落等特点,对目标检测和分类有很大影响。因此,本文根据28种害虫的体型和颜色,将其大致分为7个A~G种,使用ylo - v5l模型对其进行检测和计数,然后将检测结果代入ResNet50识别模型,确定其种类。该方法大大降低了农田有害生物检测的误检率。此外,本文还提出了一种预测增强算法。待检测的害虫图像经过增强后,分别带入识别模型,对识别结果进行加权平均,得到最终结果。mAP.5:。单个YOLO-V5l模型的准确率为71.4%,平均准确率为80.91%,漏检率为5.39%。本文提出的病虫害检测模型平均准确率为89.56%,提高了农田病虫害的识别精度。该模型将改善原有人工统计的不足,促进中国智慧农业的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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