Determination of tomato leafminer: Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) damage on tomato using deep learning instance segmentation method

Abstract

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.

Adaptive Detection and Classification of Brain Tumour Images Based on Photoacoustic Imaging

“…Existing detection segmentation methods suffer from the lack of effective adaptivity to adequately adapt to different scenes and data feature changes. The YoLov8-MedSAM model proposed in this paper is based on the YoLov8 algorithm [26][27][28] for the target detection of photoacoustic brain tumour images which accurately locates and labels brain tumour…”

“…Existing detection segmentation methods suffer from the lack of effective adaptivity to adequately adapt to different scenes and data feature changes. The YoLov8-MedSAM model proposed in this paper is based on the YoLov8 algorithm [26][27][28] for the target detection of photoacoustic brain tumour images which accurately locates and labels brain tumour Using k-wave simulation to obtain detailed information about tissue structure and density from CT and MRI, the images are processed, and their grey value information is used to define the initial pressure distribution during the simulation process, thus generating photoacoustic contrast from CT or MRI, as shown below: Firstly, the original images are loaded and processed, and the initial pressure distributions are defined based on their information mapping. Secondly, the simulation parameters are set up, defining the geometry and medium parameters of the acoustic field, the position of the transducers, and the number of them.…”

“…Existing detection segmentation methods suffer from the lack of effective adaptivity to adequately adapt to different scenes and data feature changes. The YoLov8-MedSAM model proposed in this paper is based on the YoLov8 algorithm [26][27][28] for the target detection of photoacoustic brain tumour images which accurately locates and labels brain tumour regions and subsequently uses the MedSAM model to directly segment the labelled regions with different brain tumour sizes and shapes without the need to input the user-drawn bounding box. The YoLov8-MedSAM model proposed in this paper can effectively solve the problem of the lack of adaptivity of existing methods, achieve the accurate detection and segmentation of photoacoustic brain tumour images, and provide an efficient and reliable auxiliary tool for medical imaging diagnosis, and its model overview diagram is shown in Figure 3.…”

“…The YoLov8 model [28,30,31] consists of four components: input, Backbone, Neck, and Head. Among them, Backbone is responsible for feature extraction and employs the C2f module and SPPF for feature fusion [32]; Neck employs FPN and PAN to achieve multi-scale feature fusion; the Head component switches from an anchor-based to an anchor-free approach and uses task-oriented allocators to perform positive-negative sample matching.…”

“…The YOLO model [29] is different from the two-stage detectors of the R-CNN family, and the single detector is more focused on predicting the position and category of the object at the same time, which can be focused on predicting both the position and category of objects at the same time and achieve faster and high detection accuracy, which is suitable for various application scenarios of real-time target detection tasks. The YoLov8 model [28,30,31] consists of four components: input, Backbone, Neck, and Head. Among them, Backbone is responsible for feature extraction and employs the C2f module and SPPF for feature fusion [32]; Neck employs FPN and PAN to achieve multiscale feature fusion; the Head component switches from an anchor-based to an anchor-free approach and uses task-oriented allocators to perform positive-negative sample matching.…”