Accurate detection and depth estimation of table grapes and peduncles for robot harvesting, combining monocular depth estimation and CNN methods

Coll-Ribes, Gabriel; Torres-Rodríguez, Iván J.; Grau, Antoni; Guerra, Edmundo; Sanfeliu, Alberto

doi:10.1016/j.compag.2023.108362

Cited by 23 publications

(3 citation statements)

References 35 publications

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“…This method outperforms state-of-the-art techniques on the WGISD and CANOPIES datasets using instantaneous segmentation and monocular depth estimation with CNNs. RGB-D data exceeds RGB data, making it promising for precision agriculture [15].…”

Section: Related Workmentioning

confidence: 99%

Implementation of Flower Recognition using Convolutional Neural Networks

Hindarto,

Amalia

2023

ijsecs

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The recognition of flowers holds significant importance within the realms of ecological research, horticulture, and diverse technological applications. This study presents "Blossom Insight," an innovative methodology for flower identification that employs Convolutional Neural Networks within the Keras framework. This study aims to examine the necessity of precise and effective flower categorization, considering the extensive range of floral species. The methodology encompasses a rigorous procedure of data preprocessing, utilizing sophisticated techniques to augment the model's capacity to distinguish intricate characteristics of flowers. The crux of the study revolves around the amalgamation of a Convolutional Neural Network, a robust deep learning methodology, with Keras, a user-accessible open-source framework for machine learning. The integration of these components enables the development of a resilient flower recognition model that possesses the ability to acquire complex patterns and characteristics from input images. The training of the model encompasses exposure to a wide range of flower datasets, which enhances its ability to generalize across different species and environmental conditions effectively. The findings illustrate the effectiveness of "Blossom Insight" in attaining a notable level of precision in tasks related to the identification of flowers. The implementation not only contributes to the advancement of the field of computer vision but also offers a valuable resource for researchers, horticulturists, and enthusiasts seeking a comprehensive understanding and accurate identification of floral species. The development of "Blossom Insight" signifies a notable advancement in utilizing deep learning techniques to augment our understanding and admiration of the wide variety present in the realm of flowers.

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Section: Related Workmentioning

confidence: 99%

Implementation of Flower Recognition using Convolutional Neural Networks

Hindarto,

Amalia

2023

ijsecs

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“…State-of-the-art fruit detection relies on fully convolutional neural networks (CNNs) for an optimal speed-precision balance [22]. Furthermore, integrating depth estimation can strain processing resources, requiring either stereo systems [23][24][25], LIDAR sensors [26], or dedicated monocular networks [27][28][29]. In this sense, this work presents a novel Depth Object Detector (DOD) method: a deep-learning-based lightweight object detection algorithm with monocular depth estimation for cost-effective systems and real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming

Jaramillo-Hernández,

Julian,

Marco-Detchart

et al. 2024

Sensors

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In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer vision and artificial intelligence method for object detection with integrated depth estimation. With applications ranging from autonomous fruit-harvesting systems to phenotyping tasks, the proposed Depth Object Detector (DOD) is trained and evaluated using the Microsoft Common Objects in Context dataset and the MinneApple dataset for object and fruit detection, respectively. The DOD is benchmarked against current state-of-the-art models. The results demonstrate the proposed method’s efficiency for operation on embedded systems, with a favorable balance between accuracy and speed, making it well suited for real-time applications on edge devices in the context of the Internet of things.

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“…However, the diameter of Xiaomila stems is very small (1–3 mm), and the background is complex. Traditional stereo cameras and depth sensors such as lidar have been proven to be unable to provide reliable depth information ( Coll-Ribes et al., 2023 ). To solve these problems, this study mainly makes the following contributions:…”

Section: Introductionmentioning

confidence: 99%

A lightweight Yunnan Xiaomila detection and pose estimation based on improved YOLOv8

Wang,

Tang,

Gong

et al. 2024

Front. Plant Sci.

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IntroductionYunnan Xiaomila is a pepper variety whose flowers and fruits become mature at the same time and multiple times a year. The distinction between the fruits and the background is low and the background is complex. The targets are small and difficult to identify.MethodsThis paper aims at the problem of target detection of Yunnan Xiaomila under complex background environment, in order to reduce the impact caused by the small color gradient changes between xiaomila and background and the unclear feature information, an improved PAE-YOLO model is proposed, which combines the EMA attention mechanism and DCNv3 deformable convolution is integrated into the YOLOv8 model, which improves the model’s feature extraction capability and inference speed for Xiaomila in complex environments, and achieves a lightweight model. First, the EMA attention mechanism is combined with the C2f module in the YOLOv8 network. The C2f module can well extract local features from the input image, and the EMA attention mechanism can control the global relationship. The two complement each other, thereby enhancing the model’s expression ability; Meanwhile, in the backbone network and head network, the DCNv3 convolution module is introduced, which can adaptively adjust the sampling position according to the input feature map, contributing to stronger feature capture capabilities for targets of different scales and a lightweight network. It also uses a depth camera to estimate the posture of Xiaomila, while analyzing and optimizing different occlusion situations. The effectiveness of the proposed method was verified through ablation experiments, model comparison experiments and attitude estimation experiments.ResultsThe experimental results indicated that the model obtained an average mean accuracy (mAP) of 88.8%, which was 1.3% higher than that of the original model. Its F1 score reached 83.2, and the GFLOPs and model sizes were 7.6G and 5.7MB respectively. The F1 score ranked the best among several networks, with the model weight and gigabit floating-point operations per second (GFLOPs) being the smallest, which are 6.2% and 8.1% lower than the original model. The loss value was the lowest during training, and the convergence speed was the fastest. Meanwhile, the attitude estimation results of 102 targets showed that the orientation was correctly estimated exceed 85% of the cases, and the average error angle was 15.91°. In the occlusion condition, 86.3% of the attitude estimation error angles were less than 40°, and the average error angle was 23.19°.DiscussionThe results show that the improved detection model can accurately identify Xiaomila targets fruits, has higher model accuracy, less computational complexity, and can better estimate the target posture.

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Accurate detection and depth estimation of table grapes and peduncles for robot harvesting, combining monocular depth estimation and CNN methods

Cited by 23 publications

References 35 publications

Implementation of Flower Recognition using Convolutional Neural Networks

Implementation of Flower Recognition using Convolutional Neural Networks

Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming

A lightweight Yunnan Xiaomila detection and pose estimation based on improved YOLOv8

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