A Grape Dataset for Instance Segmentation and Maturity Estimation

Blekos, Achilleas; Chatzis, Konstantinos; Kotaidou, Martha; Chatzis, Theocharis; Solachidis, Vassilios; Konstantinidis, Dimitrios; Dimitropoulos, Kosmas

doi:10.3390/agronomy13081995

Cited by 9 publications

(1 citation statement)

References 62 publications

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“…Compared to traditional machine learning methods, deep learning exhibits superior performance in handling large-scale data, enabling the extraction of underlying patterns and enhancing model generalization capabilities [19]. In the realm of grape variety identification, deep learning not only enhances identification accuracy but also adapts well to variations in grape varieties across different regions, climates, and growing conditions, thereby exhibiting enhanced robustness [20]. Although deep learning has shown strong potential for grape variety identification, the wine industry still faces many challenges.…”

Section: Introductionmentioning

confidence: 99%

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion

Peng,

Ouyang,

Peng

et al. 2024

Sensors

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Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

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Section: Introductionmentioning

confidence: 99%

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion

Peng,

Ouyang,

Peng

et al. 2024

Sensors

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WS-YOLO: An Agronomical and Computer Vision-Based Framework to Detect Drought Stress in Lettuce Seedlings Using IR Imaging and YOLOv8

Wolter-Salas,

Canessa,

Campos-Vargas

et al. 2023

Communications in Computer and Information Science

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

Uygun,

Ozguven

2024

Eur Food Res Technol

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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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A Grape Dataset for Instance Segmentation and Maturity Estimation

Cited by 9 publications

References 62 publications

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion

WS-YOLO: An Agronomical and Computer Vision-Based Framework to Detect Drought Stress in Lettuce Seedlings Using IR Imaging and YOLOv8

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

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