利用深度学习实例分割方法确定番茄潜叶蝇:利用深度学习实例分割方法确定番茄叶蝉:Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) 的危害情况

IF 3 3区 农林科学 Q2 FOOD SCIENCE & TECHNOLOGY European Food Research and Technology Pub Date : 2024-04-04 DOI:10.1007/s00217-024-04516-w
Tahsin Uygun, Mehmet Metin Ozguven
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引用次数: 0

摘要

害虫对农业生产中的产品产量和质量有很大的负面影响。农业生产者可能无法准确识别害虫和害虫危害迹象。因此,可能会使用不正确或过量的杀虫剂。过量使用杀虫剂不仅会造成人类健康和环境污染,还会增加投入成本。因此,害虫的早期检测和诊断极为重要。在本研究中,研究了基于深度学习的实例分割方法在温室条件下早期检测番茄植株叶片部分 T. absoluta 害虫危害的有效性。通过获取 800 幅温室条件下的健康和受损图像,创建了一个原始数据集。采集到的图像被标记为边界框,并通过 Segment Anything Model(SAM)模型自动转换为掩膜标签。创建的数据集使用 YOLOv8(n/s/m/l/x)-Seg 模型进行训练。训练结果表明,在 mAP0.5 指标中,所提议的 YOLOv8l-Seg 模型的箱体性能为 0.924。YOLOv8l-Seg 模型的掩码值分别为:mAP0.5、mAP0.5-0.95、Precision、Recall,表现最佳,分别为 0.935、0.806、0.956 和 0.859。然后,使用不同数据输入尺寸训练的 YOLOv8l-Seg 模型在 640 × 640 尺寸下表现最佳,而在 80 × 80 尺寸下表现最低,mAP0.5 指标值为 0.699。使用 YOLOv7、YOLOv5l、YOLACT 和 Mask R-CNN 实例分割模型对同一数据集进行了训练,并与 YOLOv8l-Seg 模型进行了性能比较。结果表明,YOLOv8l-Seg 模型是检测番茄植株中 T. absoluta 危害的最佳模型。Mask R-CNN 模型的性能最低,指标为 0.806 mAP0.5。这项研究的结果表明,所提出的模型和方法可以有效地用于检测 T. absoluta 害虫造成的损害。
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Determination of tomato leafminer: Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) damage on tomato using deep learning instance segmentation method

Pests significantly negatively affect product yield and quality in agricultural production. Agricultural producers may not accurately identify pests and signs of pest damage. Thus, incorrect or excessive insecticides may be used. Excessive use of insecticides not only causes human health and environmental pollution, but also increases input costs. Therefore, early detection and diagnosis of pests is extremely important. In this study, the effectiveness of the instance segmentation method, a deep learning-based method, was investigated for the early detection of the damage caused by the T. absoluta pest in the leaf part of the tomato plant under greenhouse conditions. An original dataset was created by acquiring 800 healthy and damaged images under greenhouse conditions. The acquired images were labelled as bounding box and automatically converted to a mask label with the Segment Anything Model (SAM) model. The created dataset was trained with YOLOv8(n/s/m/l/x)-Seg models. As a result of the training, the box performance of the proposed YOLOv8l-Seg model was measured as 0.924 in the mAP0.5 metric. The YOLOv8l-Seg model mask values are, respectively: mAP0.5, mAP0.5–0.95, Precision, Recall showed the best performance with values of 0.935, 0.806, 0.956 and 0.859. Then, the YOLOv8l-Seg model, trained with different data input sizes, showed the best performance at 640 × 640 size and the lowest performance with a value of 0.699 in the  mAP0.5 metric in the 80 × 80 size. The same dataset was trained with YOLOv7, YOLOv5l, YOLACT and Mask R-CNN instance segmentation models and performance comparisons were made with the YOLOv8l-Seg model. As a result, it was determined that the model that best detected T. absoluta damage in tomato plants was the YOLOv8l-Seg model. The Mask R-CNN model showed the lowest performance with a metric of 0.806 mAP0.5. The results obtained from this study revealed that the proposed model and method can be used effectively in detecting the damage caused by the T. absoluta pest.

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来源期刊
European Food Research and Technology
European Food Research and Technology 工程技术-食品科技
CiteScore
6.60
自引率
3.00%
发文量
232
审稿时长
2.0 months
期刊介绍: The journal European Food Research and Technology publishes state-of-the-art research papers and review articles on fundamental and applied food research. The journal''s mission is the fast publication of high quality papers on front-line research, newest techniques and on developing trends in the following sections: -chemistry and biochemistry- technology and molecular biotechnology- nutritional chemistry and toxicology- analytical and sensory methodologies- food physics. Out of the scope of the journal are: - contributions which are not of international interest or do not have a substantial impact on food sciences, - submissions which comprise merely data collections, based on the use of routine analytical or bacteriological methods, - contributions reporting biological or functional effects without profound chemical and/or physical structure characterization of the compound(s) under research.
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