Automatic detection of insect predation through the segmentation of damaged leaves

Vieira, Gabriel da Silva; Rocha, Bruno Moraes; Fonseca, Afonso U.; Sousa, Naiane Maria de; Ferreira, Julio Cesar; Cabacinha, Christian Dias; Soares, Fabrízzio

doi:10.1016/j.atech.2022.100056

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…This framework enabled the accurate segmentation and detection of potato leaf diseases. However, instance segmentation mainly aims at pest-infected regions with apparent boundaries, which is less effective when addressing leaf pest-infected regions with blurred boundaries [34]. Therefore, the pest-infected region detection domain needs more effective detection methods.…”

Section: Image Classification and Instance Segmentation Detectorsmentioning

confidence: 99%

Research on Polygon Pest-Infected Leaf Region Detection Based on YOLOv8

Zhu,

Hao,

2023

Agriculture

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Object detection in deep learning provides a viable solution for detecting crop-pest-infected regions. However, existing rectangle-based object detection methods are insufficient to accurately detect the shape of pest-infected regions. In addition, the method based on instance segmentation has a weak ability to detect the pest-infected regions at the edge of the leaves, resulting in unsatisfactory detection results. To solve these problems, we constructed a new polygon annotation dataset called PolyCorn, designed specifically for detecting corn leaf pest-infected regions. This was made to address the scarcity of polygon object detection datasets. Building upon this, we proposed a novel object detection model named Poly-YOLOv8, which can accurately and efficiently detect corn leaf pest-infected regions. Furthermore, we designed a loss calculation algorithm that is insensitive to ordering, thereby enhancing the robustness of the model. Simultaneously, we introduced a loss scaling factor based on the perimeter of the polygon, improving the detection ability for small objects. We constructed comparative experiments, and the results demonstrate that Poly-YOLOv8 outperformed other models in detecting irregularly shaped pest-infected regions, achieving 67.26% in mean average precision under 0.5 threshold (mAP50) and 128.5 in frames per second (FPS).

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Section: Image Classification and Instance Segmentation Detectorsmentioning

confidence: 99%

Research on Polygon Pest-Infected Leaf Region Detection Based on YOLOv8

Zhu,

Hao,

2023

Agriculture

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“…The research on pest detection and classification mainly focuses on improving the classical deep learning methods, such as P. Venk et al [10], which achieved good results on pest datasets of three peanut crops by integrating VIT, PCA, and MFO; Pattnaik G et al [11], which feature extraction of pests by HOG and LBP, and the extracted feature maps are fed into SVM [12] classifiers for training; In contrast, leaf damage caused by insect pests can be detected in two ways: by quantifying the extent of insect damage to the leaf and by detecting the location of the insect-damaged leaf. For example, Liang et al [13] developed polynomial and logistic regression models for leaf extraction to estimate leaf damage; Da Silva et al [14] used image segmentation to preserve the leaf region, augmented the dataset with a synthesis technique, and trained the network with a model for detecting pest-induced damage to leaves; Fang et al [15], Zhu R et al [16], Zhu L et al [17], and others used the improved YOLO series of models to identify pest-induced leaf damage and achieved good detection results.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Tomato Pest Detection via Leaf Imagery with a New Loss Function

Mo,

Xie,

et al. 2024

Agronomy

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Pests have caused significant losses to agriculture, greatly increasing the detection of pests in the planting process and the cost of pest management in the early stages. At this time, advances in computer vision and deep learning for the detection of pests appearing in the crop open the door to the application of target detection algorithms that can greatly improve the efficiency of tomato pest detection and play an important technical role in the realization of the intelligent planting of tomatoes. However, in the natural environment, tomato leaf pests are small in size, large in similarity, and large in environmental variability, and this type of situation can lead to greater detection difficulty. Aiming at the above problems, a network target detection model based on deep learning, YOLONDD, is proposed in this paper. Designing a new loss function, NMIoU (Normalized Wasserstein Distance with Mean Pairwise Distance Intersection over Union), which improves the ability of anomaly processing, improves the model’s ability to detect and identify objects of different scales, and improves the robustness to scale changes; Adding a Dynamic head (DyHead) with an attention mechanism will improve the detection ability of targets at different scales, reduce the number of computations and parameters, improve the accuracy of target detection, enhance the overall performance of the model, and accelerate the training process. Adding decoupled head to Head can effectively reduce the number of parameters and computational complexity and enhance the model’s generalization ability and robustness. The experimental results show that the average accuracy of YOLONDD can reach 90.1%, which is 3.33% higher than the original YOLOv5 algorithm and is better than SSD, Faster R-CNN, YOLOv7, YOLOv8, RetinaNet, and other target detection networks, and it can be more efficiently and accurately utilized in tomato leaf pest detection.

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