Greenhouse tomato detection and pose classification algorithm based on improved YOLOv5

Zhang, Junxiong; Xie, Jinyi; Zhang, Fan; Gao, Jin; Yang, Chen; Song, Chaoyu; Rao, Weijie; Zhang, Yu

doi:10.1016/j.compag.2023.108519

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…In recent years, data-driven approaches based on learning features from the data, including those based on object detection architectures, such as Mask-RCNN [10] or the YOLO family of object detectors [11], have become prominent. These applications are diverse and have included objects such as tomatoes [12][13][14][15][16][17][18][19], peppers [20], and peduncles [16,21], as well as branches and stems [22][23][24].…”

Section: Object Detection For Greenhouse Processesmentioning

confidence: 99%

Object Detection in Tomato Greenhouses: A Study on Model Generalization

Haggag,

Veres,

Tarry

et al. 2024

Agriculture

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Harvesting operations in agriculture are labour-intensive tasks. Automated solutions can help alleviate some of the pressure faced by rising costs and labour shortage. Yet, these solutions are often difficult and expensive to develop. To enable the use of harvesting robots, machine vision must be able to detect and localize target objects in a cluttered scene. In this work, we focus on a subset of harvesting operations, namely, tomato harvesting in greenhouses, and investigate the impact that variations in dataset size, data collection process and other environmental conditions may have on the generalization ability of a Mask-RCNN model in detecting two objects critical to the harvesting task: tomatoes and stems. Our results show that when detecting stems from a perpendicular perspective, models trained using data from the same perspective are similar to one that combines both perpendicular and angled data. We also show larger changes in detection performance across different dataset sizes when evaluating images collected from an angled camera perspective, and overall larger differences in performance when illumination is the primary source of variation in the data. These findings can be used to help practitioners prioritize data collection and evaluation efforts, and lead to larger-scale harvesting dataset construction efforts.

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Section: Object Detection For Greenhouse Processesmentioning

confidence: 99%

Object Detection in Tomato Greenhouses: A Study on Model Generalization

Haggag,

Veres,

Tarry

et al. 2024

Agriculture

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show abstract

“…The accuracy reached 95.07%, which is better than the traditional RGB-based CNN and machine learning models. Zhang et al [18] proposed a YOLOv5-based visual detection and pose classification algorithm to detect tomatoes and were able to identify the ripeness of tomatoes. The above methods and models have been successful in fruit target detection and ripeness classifying.…”

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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“…For example, in medicine, image data can be used to predict different diseases, such as cancer [1,2], glaucoma [3,4], and pneumonia [5,6]. Object detection models can be used in systems for travel direction recommendation [7], in industry for solutions to robotization tasks [8,9], in face detection for different applications [10,11], or other fields [12][13][14][15][16]. Usually, in all research, various computer vision methods or combinations are used to solve the specific problem.…”

Section: Introductionmentioning

confidence: 99%

The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details

Kvietkauskas,

Pavlov,

Stefanovič

et al. 2024

Applied Sciences

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Computer vision solutions have become widely used in various industries and as part of daily solutions. One task of computer vision is object detection. With the development of object detection algorithms and the growing number of various kinds of image data, different problems arise in relation to the building of models suitable for various solutions. This paper investigates the influence of parameters used in the training process involved in detecting similar kinds of objects, i.e., the hyperparameters of the algorithm and the training parameters. This experimental investigation focuses on the widely used YOLOv5 algorithm and analyses the performance of different models of YOLOv5 (n, s, m, l, x). In the research, the newly collected construction details (22 categories) dataset is used. Experiments are performed using pre-trained models of the YOLOv5. A total of 185 YOLOv5 models are trained and evaluated. All models are tested on 3300 images photographed on three different backgrounds: mixed, neutral, and white. Additionally, the best-obtained models are evaluated using 150 new images, each of which has several dozen construction details and is photographed against different backgrounds. The deep analysis of different YOLOv5 models and the hyperparameters shows the influence of various parameters when analysing the object detection of similar objects. The best model was obtained when the YOLOv5l was used and the parameters are as follows: coloured images, image size—320; batch size—32; epoch number—300; layers freeze option—10; data augmentation—on; learning rate—0.001; momentum—0.95; and weight decay—0.0007. These results may be useful for various tasks in which small and similar objects are analysed.

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Greenhouse tomato detection and pose classification algorithm based on improved YOLOv5

Cited by 7 publications

References 20 publications

Object Detection in Tomato Greenhouses: A Study on Model Generalization

Object Detection in Tomato Greenhouses: A Study on Model Generalization

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

The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details

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