Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models

Phan, Quoc-Hung; Nguyen, Van-Tung; Lien, Chi-Hsiang; Duong, The-Phong; Hou, Max Ti-Kuang; Le, Ngoc-Bich

doi:10.3390/plants12040790

Cited by 25 publications

(16 citation statements)

References 40 publications

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“…The category "Other Fruits" corresponds to fruits that are analyzed in a single article, usually fruits of local origin. Agrivita 1 [115] Applied Intelligence 1 [116] Computational Intelligence and Neuroscience 1 [117] Computer Systems Science and Engineering 1 [118] Computers Electrical Engineering 1 [119] Data in Brief 1 [42] Ecological Informatics 1 [120] Electronics 1 [121] Energy Reports 1 [122] European Food Research and Technology 1 [123] Expert Systems With Applications 1 [124] Frontiers in Robotics and AI 1 [125] Horticulturae 1 [126] Journal of Robotics and Mechatronics 1 [135] Jurnal Kejuruteraan 1 [136] Mathematical Problems in Engineering 1 [137] Continued on next page Mechanical Systems and Signal Processing 1 [139] Multimedia Systems 1 [140] Neural Computing Applications 1 [141] Neural Network World 1 [142] Neural Networks 1 [143] Neurocomputing 1 [144] Plant and Cell Physiology 1 [145] Plants 1 [146] Postharvest Biology and Technology 1 [147] Procedia Computer Science 1 [148] Remote Sensing 1 [149] Results in Engineering 1 [150] Scientia Horticulturae 1 [151] Scientific African 1 [152] Scientific Programming 1 [153] Scientific Reports 1 [154] Sn Applied Sciences 1 [50] Sustainability 1 [155] Traitement du Signal 1 [156] Visual Computer 1 [52]…”

Section: F Publication Metadatamentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

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The application of Deep Learning models in fruit analysis has garnered significant attention due to its potential to revolutionize the agricultural sector and enhance crop monitoring. This paper presents a comprehensive review of recent research efforts in fruit analysis using Deep Learning techniques. The study delves into model selection, dataset characteristics, evaluation metrics, challenges, and future directions in this domain. Various model architectures, including classical Convolutional Neural Networks (CNNs) and advanced detection models like R-CNN and YOLO, have been explored for tasks ranging from fruit classification to detection. Evaluation metrics such as precision, recall, F1-score, and mean Average Precision (mAP) have been commonly used to assess model performance. Challenges, including data scarcity, labeling complexities, variations in fruit characteristics, and computational efficiency, have been identified and discussed. The paper also presents an overview of available datasets, encompassing both proprietary and publicly accessible sources. Future research directions involve exploring enhanced data augmentation, multi-modal integration, knowledge transfer across species, robustness in dynamic environments, improved computational efficiency, and practical integration of models into real-world agricultural systems. This review provides valuable insights for researchers and practitioners aiming to leverage Deep Learning for fruit analysis in the pursuit of sustainable agriculture and food production.

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Section: F Publication Metadatamentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

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“…Typical single-stage methods include the YOLO ("you only look once") series [12], the SSD (single-shot multi-box detector) series [13], and others. Phan et al [14] proposed four deep learning frameworks, Yolov5m, and models combining ResNet50, ResNet-101, and EfficientNet-B0, for classifying tomato fruits on the vine into ripe, unripe, and damaged categories. Azadnia et al [15] classified hawthorn images into unripe, ripe, and overripe using Inception-V3, ResNet-50, and DL models.…”

Section: Introductionmentioning

confidence: 99%

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Wang,

Han

et al. 2024

Agriculture

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As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

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“…The advent of deep learning has provided new ways to solve problems in image recognition [8], [9]. Convolutional neural networks have achieved remarkable success in image classification tasks, with performance exceeding that of humans.…”

Section: Introductionmentioning

confidence: 99%

Apple Leaf Disease Diagnosis Based on Knowledge Distillation and Attention Mechanism

Dong,

Gu,

Chen

et al. 2024

IEEE Access

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Accurately diagnosing apple leaf diseases can reduce the use of pesticides and improve the quality of fruits, which is of significance to smart agriculture. Convolutional neural network as a deep learning model is widely used in the field of intelligent diagnosis of apple leaf diseases. Deploying a deep neural network for apple disease diagnosis to mobile devices allows for smarter, more efficient, and more accurate disease identification. However, classical convolutional neural networks have some limitations on agricultural disease diagnosis, such as a huge number of parameters, heavy computation, and long inference time. Thus, such a complex deep learning model is not easily deployed to mobile devices. To address the above problems, we propose the ECA-KDNet, an improved lightweight model based on the ECA attention mechanism and knowledge distillation, which shows superiority in accuracy, robustness, and lightweight. The experimental results show that compared with the classical convolutional neural network models, ECA-KDNet improves accuracy (98.28%) while ensuring lightweight (3.38 M).

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Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models

Cited by 25 publications

References 40 publications

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Apple Leaf Disease Diagnosis Based on Knowledge Distillation and Attention Mechanism

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