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16 pages, 5187 KiB

Open AccessArticle

Development of a Premium Tea-Picking Robot Incorporating Deep Learning and Computer Vision for Leaf Detection

by Luofa Wu, Helai Liu, Chun Ye and Yanqi Wu

Appl. Sci. 2024, 14(13), 5748; https://doi.org/10.3390/app14135748 - 1 Jul 2024

Viewed by 1150

Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of [...] Read more.

Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot’s performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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16 pages, 12098 KiB

Open AccessArticle

Enhanced Pest Recognition Using Multi-Task Deep Learning with the Discriminative Attention Multi-Network

by Zhaojie Dong, Xinyu Wei, Yonglin Wu, Jiaming Guo and Zhixiong Zeng

Appl. Sci. 2024, 14(13), 5543; https://doi.org/10.3390/app14135543 - 26 Jun 2024

Cited by 1 | Viewed by 1155

Abstract

Accurate recognition of agricultural pests is crucial for effective pest management and reducing pesticide usage. In recent research, deep learning models based on residual networks have achieved outstanding performance in pest recognition. However, challenges arise from complex backgrounds and appearance changes throughout the [...] Read more.

Accurate recognition of agricultural pests is crucial for effective pest management and reducing pesticide usage. In recent research, deep learning models based on residual networks have achieved outstanding performance in pest recognition. However, challenges arise from complex backgrounds and appearance changes throughout the pests’ life stages. To address these issues, we develop a multi-task learning framework utilizing the discriminative attention multi-network (DAM-Net) for the main task of recognizing intricate fine-grained features. Additionally, our framework employs the residual network-50 (ResNet-50) for the subsidiary task that enriches texture details and global contextual information. This approach enhances the main task with comprehensive features, improving robustness and precision in diverse agricultural scenarios. An adaptive weighted loss mechanism dynamically adjusts task loss weights, further boosting overall accuracy. Our framework achieves accuracies of 99.7% on the D0 dataset and 74.1% on the IP102 dataset, demonstrating its efficacy in training high-performance pest-recognition models. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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18 pages, 11865 KiB

Open AccessArticle

Target Detection for Coloring and Ripening Potted Dwarf Apple Fruits Based on Improved YOLOv7-RSES

by Haoran Ma, Yanwen Li, Xiaoying Zhang, Yaoyu Li, Zhenqi Li, Runqing Zhang, Qian Zhao and Renjie Hao

Appl. Sci. 2024, 14(11), 4523; https://doi.org/10.3390/app14114523 - 24 May 2024

Cited by 2 | Viewed by 1059

Abstract

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing [...] Read more.

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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16 pages, 13259 KiB

Open AccessArticle

A Novel Deep Learning Method for Detecting Strawberry Fruit

by Shuo Shen, Famin Duan, Zhiwei Tian and Chunxiao Han

Appl. Sci. 2024, 14(10), 4213; https://doi.org/10.3390/app14104213 - 16 May 2024

Viewed by 1293

Abstract

The recognition and localization of strawberries are crucial for automated harvesting and yield prediction. This article proposes a novel RTF-YOLO (RepVgg-Triplet-FocalLoss-YOLO) network model for real-time strawberry detection. First, an efficient convolution module based on structural reparameterization is proposed. This module was integrated into [...] Read more.

The recognition and localization of strawberries are crucial for automated harvesting and yield prediction. This article proposes a novel RTF-YOLO (RepVgg-Triplet-FocalLoss-YOLO) network model for real-time strawberry detection. First, an efficient convolution module based on structural reparameterization is proposed. This module was integrated into the backbone and neck networks to improve the detection speed. Then, the triplet attention mechanism was embedded into the last two detection heads to enhance the network’s feature extraction for strawberries and improve the detection accuracy. Lastly, the focal loss function was utilized to enhance the model’s recognition capability for challenging strawberry targets, which thereby improves the model’s recall rate. The experimental results demonstrated that the RTF-YOLO model achieved a detection speed of 145 FPS (frames per second), a precision of 91.92%, a recall rate of 81.43%, and an

m A P

(mean average precision) of 90.24% on the test dataset. Relative to the baseline of YOLOv5s, it showed improvements of 19%, 2.3%, 4.2%, and 3.6%, respectively. The RTF-YOLO model performed better than other mainstream models and addressed the problems of false positives and false negatives in strawberry detection caused by variations in illumination and occlusion. Furthermore, it significantly enhanced the speed of detection. The proposed model can offer technical assistance for strawberry yield estimation and automated harvesting. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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16 pages, 8702 KiB

Open AccessArticle

An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm

by Fangbin Wang, Zini Wang, Zhong Chen, Darong Zhu, Xue Gong and Wanlin Cong

Appl. Sci. 2023, 13(19), 11031; https://doi.org/10.3390/app131911031 - 7 Oct 2023

Cited by 4 | Viewed by 1838

Abstract

To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this [...] Read more.

To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this paper. Firstly, the edge image features of hot spots were extracted based on residual neural networks. Secondly, by combining the feature pyramid structure, an edge-guided feature pyramid structure was designed, and the hot spot edge features were injected into a Mask R-CNN network. Thirdly, an infrared spatial attention module was introduced into the Mask R-CNN network when feature extraction and the infrared features of the detected hot spots were enhanced. Fourthly, the size ratio of the candidate frames was adjusted self-adaptively according to the structural characteristics of the aspect ratio of the hot spots. Finally, the validation experiments were conducted, and the results demonstrated that the hot spot contours of thermal infrared images were enhanced through the algorithm proposed in this paper, and the segmentation accuracy was significantly improved. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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14 pages, 2813 KiB

Open AccessArticle

Generating Image Descriptions of Rice Diseases and Pests Based on DeiT Feature Encoder

by Chunxin Ma, Yanrong Hu, Hongjiu Liu, Ping Huang, Yikun Zhu and Dan Dai

Appl. Sci. 2023, 13(18), 10005; https://doi.org/10.3390/app131810005 - 5 Sep 2023

Cited by 2 | Viewed by 1257

Abstract

We propose a DeiT (Data-Efficient Image Transformer) feature encoder-based algorithm for identifying disease types and generating relevant descriptions of diseased crops. It solves the scarcity problem of the image description algorithm applied in agriculture. We divided the original image into a sequence of [...] Read more.

We propose a DeiT (Data-Efficient Image Transformer) feature encoder-based algorithm for identifying disease types and generating relevant descriptions of diseased crops. It solves the scarcity problem of the image description algorithm applied in agriculture. We divided the original image into a sequence of image patches to fit the input form of the DeiT encoder, which was distilled by RegNet. Then, we used the Transformer decoder to generate descriptions. Compared to “CNN + LSTM” models, our proposed model is entirely convolution-free and has high training efficiency. On the Rice2k dataset created by us, the model achieved a 47.3 BLEU-4 score, 65.0 ROUGE_L score, and 177.1 CIDEr score. The extensive experiments demonstrate the effectiveness and the strong robustness of our model. It can be better applied to automatically generate descriptions of similar crop disease characteristics. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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13 pages, 1851 KiB

Open AccessArticle

Exploration of Machine Learning Algorithms for pH and Moisture Estimation in Apples Using VIS-NIR Imaging

by Erhan Kavuncuoğlu, Necati Çetin, Bekir Yildirim, Mohammad Nadimi and Jitendra Paliwal

Appl. Sci. 2023, 13(14), 8391; https://doi.org/10.3390/app13148391 - 20 Jul 2023

Cited by 2 | Viewed by 1448

Abstract

Non-destructive assessment of fruits for grading and quality determination is essential to automate pre- and post-harvest handling. Near-infrared (NIR) hyperspectral imaging (HSI) has already established itself as a powerful tool for characterizing the quality parameters of various fruits, including apples. The adoption of [...] Read more.

Non-destructive assessment of fruits for grading and quality determination is essential to automate pre- and post-harvest handling. Near-infrared (NIR) hyperspectral imaging (HSI) has already established itself as a powerful tool for characterizing the quality parameters of various fruits, including apples. The adoption of HSI is expected to grow exponentially if inexpensive tools are made available to growers and traders at the grassroots levels. To this end, the present study aims to explore the feasibility of using a low-cost visible-near-infrared (VIS-NIR) HSI in the 386–1028 nm wavelength range to predict the moisture content (MC) and pH of Pink Lady apples harvested at three different maturity stages. Five different machine learning algorithms, viz. partial least squares regression (PLSR), multiple linear regression (MLR), k-nearest neighbor (kNN), decision tree (DT), and artificial neural network (ANN) were utilized to analyze HSI data cubes. In the case of ANN, PLSR, and MLR models, data analysis modeling was performed using 11 optimum features identified using a Bootstrap Random Forest feature selection approach. Among the tested algorithms, ANN provided the best performance with R (correlation), and root mean squared error (RMSE) values of 0.868 and 0.756 for MC and 0.383 and 0.044 for pH prediction, respectively. The obtained results indicate that while the VIS-NIR HSI promises success in non-destructively measuring the MC of apples, its performance for pH prediction of the studied apple variety is poor. The present work contributes to the ongoing research in determining the full potential of VIS-NIR HSI technology in apple grading, maturity assessment, and shelf-life estimation. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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17 pages, 3260 KiB

Open AccessArticle

Determination of the Live Weight of Farm Animals with Deep Learning and Semantic Segmentation Techniques

by Erdal Guvenoglu

Appl. Sci. 2023, 13(12), 6944; https://doi.org/10.3390/app13126944 - 8 Jun 2023

Cited by 5 | Viewed by 3373

Abstract

In cattle breeding, regularly taking the animals to the scale and recording their weight is important for both the performance of the enterprise and the health of the animals. This process, which must be carried out in businesses, is a difficult task. For [...] Read more.

In cattle breeding, regularly taking the animals to the scale and recording their weight is important for both the performance of the enterprise and the health of the animals. This process, which must be carried out in businesses, is a difficult task. For this reason, it is often not performed regularly or not performed at all. In this study, we attempted to estimate the weights of cattle by using stereo vision and semantic segmentation methods used in the field of computer vision together. Images of 85 animals were taken from different angles with a stereo setup consisting of two identical cameras. The distances of the animals to the camera plane were calculated by stereo distance calculation, and the areas covered by the animals in the images were determined by semantic segmentation methods. Then, using all these data, different artificial neural network models were trained. As a result of the study, it was revealed that when stereo vision and semantic segmentation methods are used together, live animal weights can be predicted successfully. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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23 pages, 30525 KiB

Open AccessArticle

A Computer Vision Milky Way Compass

by Yiting Tao, Michael Lucas, Asanka Perera, Samuel Teague, Eric Warrant and Javaan Chahl

Appl. Sci. 2023, 13(10), 6062; https://doi.org/10.3390/app13106062 - 15 May 2023

Cited by 2 | Viewed by 1606

Abstract

The Milky Way is used by nocturnal flying and walking insects for maintaining heading while navigating. In this study, we have explored the feasibility of the method for machine vision systems on autonomous vehicles by measuring the visual features and characteristics of the [...] Read more.

The Milky Way is used by nocturnal flying and walking insects for maintaining heading while navigating. In this study, we have explored the feasibility of the method for machine vision systems on autonomous vehicles by measuring the visual features and characteristics of the Milky Way. We also consider the conditions under which the Milky Way is used by insects and the sensory systems that support their detection of the Milky Way. Using a combination of simulated and real Milky Way imagery, we demonstrate that appropriate computer vision methods are capable of reliably and accurately extracting the orientation of the Milky Way under an unobstructed night sky. The technique presented achieves angular accuracy of better then

\pm 2

° under moderate light pollution conditions but also demonstrates that higher light pollution levels will adversely effect orientation estimates by systems depending on the Milky Way for navigation. Full article

(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)

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