Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model’s ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model’s parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment. 

Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision

Navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network

Tomato yield estimation relies significantly on the accurate detection of fruit quantity and size. And object detection and semantic segmentation of fruits emerge as efficacious methodologies for the realization of fruit counting and size detection. To address challenges associated with the detection and segmentation of tomato fruits in complex environments such as sample imbalance of different classes, small targets, and susceptibility to occlusion at varying stages of ripeness, this study proposed a foreground-foreground class balance method and an improved YOLOv8s network, NVW-YOLOv8s, for detecting and segmenting tomato fruits simultaneously. The foreground-foreground class balance method initially performed pixel-wise extraction on fruit samples with fewer instances. Subsequently, it synthesized them with original images containing a limited number of samples from the class, thereby augmenting the overall quantity of this specific category of fruit samples. In the NVW-YOLOv8s network, a C2f-N module, founded on the normalization-based attention module (NAM), was specifically crafted for residual feature learning. This design serves to augment the network's proficiency in extracting and integrating feature information pertaining to tomato fruits within intricate environments. Additionally, a variable focal loss (VFL) was introduced as the classification loss function to address the issue of positive and negative sample imbalance, and a regression loss function based on Wise-IoU (WIoU) was incorporated to tackle challenges related to fruit small targets and susceptibility to occlusion. The YOLOv8s models trained with the augmented balanced dataset revealed that the model's detection and segmentation performance, as indicated by [email protected], improved by 4.8 % and 5.4 %, respectively, compared with the model trained with the augmented original training dataset. The test results on the augmented balanced dataset indicated that the proposed NVW-YOLOv8s network achieved a detection [email protected] of 91.4 %, an F1-Score of 85.4 %, a segmentation [email protected] of 90.7 %, and an F1-Score of 84.8 %. These results surpass the baseline YOLOv8s network by 4.3 % and 5.5 % for detection, 4.1 % and 5.0 % for segmentation, respectively. Additionally, the processing for concurrent detection and segmentation was measured at a speed of 60.2 FPS. Therefore, the proposed method has successfully met the precision and real-time requirements for intelligent yield estimation in horticultural crops. 

NVW-YOLOv8s: An improved YOLOv8s network for real-time detection and segmentation of tomato fruits at different ripeness stages

The ability to accurately identify tomato leaves in a field setting is crucial for achieving early yield estimation, particularly with the growing importance of Precision Agriculture. It may be difficult to determine exactly what diseases are affecting tomato plants due to the overlap in symptoms between different diseases. These are the earliest signs of disease that we found in the leaves of tomato plants. Yellow leaf curl virus, leaf mold, light blight, early blight, Mosaic virus, Septoria leaf spot, and bacterial spot are just some of the seven types of plant leaf diseases that were taken into account in this paper. For the development of a testbed environment for data acquisition, the greenhouse at the university was utilized for data on the leaves of tomato plants. This study proposes a target detection model based on the improved YOLOv7 to accurately detect and categorize tomato leaves in the field. To improve the model’s feature extraction capabilities, we first incorporate the detection mechanisms SimAM and DAiAM into the framework of the baseline YOLOv7 network. To reduce the amount of information lost during the down sampling process, the max-pooling convolution (MPConv) structure is then improved. After that, this model arrived at a satisfactory outcome. Then, the image is segmented using the SIFT technique for classification, and the key regions are extracted for use in calculating feature values. After that, these data points are sent to a CNN classifier, which has a 98.8% accuracy rate and a 1.2% error rate. Finally, we compare our study to previous research to show how useful the proposed work is and to provide backing for the concept.

Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition With CNN and Improved YOLOv7

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region’s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

The accurate detection of pineapples of different maturity levels in a complex field environment is the key step to achieving the early yield estimation and mechanized picking of pineapple. This study proposes a target detection model based on the improved YOLOv7 to achieve the accurate detection and maturity classification of pineapples in the field. First, the attention mechanism SimAM is inserted into the structure of the original YOLOv7 network to improve the feature extraction ability of the model. Then, the max-pooling convolution (MPConv) structure is improved to reduce the feature loss in the downsampling process. Finally, the non-maximum suppression (NMS) algorithm is replaced by the soft-NMS algorithm, with a better effect at improving the detection effect when pineapple is in occlusion or overlaps. According to the test, the mean average precision (mAP) and recall of the model proposed in this paper are 95.82% and 89.83%, which are 2.71% and 3.41% higher than those of the original YOLOv7, respectively. The maturity classification accuracy of the model and the detection performance under six different field scenarios were analyzed quantitatively. This method provides an effective scheme for the vision system of the field pineapple picking robot.

/pdf/a-pineapple-target-detection-method-in-a-field-environment-2fenh57p.pdf

A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7

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A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7