Vision-based Vineyard Trunk Detection and its Integration into a Grapes Harvesting Robot

Badeka, Eftichia; Kalampokas, Τheofanis; Vrochidou, Εleni; Tziridis, Konstantinos; Papakostas, George A.; Pachidis, Theodore; Kaburlasos, Vassilis G.

doi:10.18178/ijmerr.10.7.374-385

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Transfer learning involves retraining a model for new classes using the existing weights (a set of weights) from a model that has already been fully trained on a specific dataset, such as ImageNet [24]. Transfer learning is effective in many sensing applications, as confirmed by the study of Monteiro et al [25]. Further steps during the DCNN network training showed that the lowest value was saved for the validation set later.…”

Section: Image Classificationmentioning

confidence: 99%

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)

Haq,

Tahir,

Lan

2023

Applied Sciences

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In the present study, we used device visualization in tandem with deep learning to detect weeds in the wheat crop system in actual time. We selected the PMAS Arid Agriculture University research farm and wheat crop fields in diverse weather environments to collect the weed images. Some 6000 images were collected for the study. Throughout the season, tfhe databank was assembled to detect the weeds. For this study, we used two different frameworks, TensorFlow and PyTorch, to apply deep learning algorithms. PyTorch’s implementation of deep learning algorithms performed comparatively better than that of TensorFlow. We concluded that the neural network implemented through the PyTorch framework achieves a superior outcome in speed and accuracy compared to other networks, such as YOLO variants. This work implemented deep learning models for weed detection using different frameworks. While working on real-time detection models, it is very important to consider the inference time and detection accuracy. Therefore, we have compared the results in terms of execution time and prediction accuracy. In particular, the accuracy of weed removal from wheat crops was judged to be 0.89 and 0.91, respectively, with inference times of 9.43 ms and 12.38 ms on the NVIDIA RTX2070 GPU for each picture (640 × 640).

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Section: Image Classificationmentioning

confidence: 99%

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)

Haq,

Tahir,

Lan

2023

Applied Sciences

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“…Crop environments present complex conditions for these tasks, which stem from various crop geometries, orientations, maturity levels, illumination, and occlusions. DL-based techniques have demonstrated higher-level feature learning and detection accuracies in comparison to traditional ML-based techniques, which makes them more applicable in complex environments (Badeka et al, 2021;Jia et al, 2021).…”

Section: Deep Learningmentioning

confidence: 99%

“…Versions of YOLO (You-Only-Look-Once) were most commonly utilized as they demonstrate high detection accuracy with quick processing times. However, these algorithms are most successful in relatively simple environments where the crop density is low, lighting is uniform, and there are few to no occlusions (Badeka et al, 2021;Bazame et al, 2021). Other solutions have developed unique algorithms to improve detection capabilities, such as Dasnet (Kang et al, 2020a) for apples, and FoveaMask (Jia et al, 2021) for green fruits.…”

Section: Deep Learningmentioning

confidence: 99%

“…For example, a robot designed for citrus fruits (Yin Variations of the YOLO algorithm were most used for object detection and localization across robotic solutions. YOLO is onestage CNN (Convolutional Neural Network) that has fast detection and high accuracy performance in real-time, but struggles with detecting small objects (Badeka et al, 2021). As mentioned in the previous section, occlusions in the natural growing environment are one of the largest limitations to the accuracy of object detection and localization in computer vision algorithms.…”

Section: Advanced Roboticsmentioning

confidence: 99%

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Leveraging I4.0 smart methodologies for developing solutions for harvesting produce

Recchia,

Urbanic

2023

Front. Manuf. Technol.

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Leveraging Computer-Aided Design (CAD) and Manufacturing (CAM) tools with advanced Industry 4.0 (I4.0) technologies presents numerous opportunities for industries to optimize processes, improve efficiency, and reduce costs. While certain sectors have achieved success in this effort, others, including agriculture, are still in the early stages of implementation. The focus of this research paper is to explore the potential of I4.0 technologies and CAD/CAM tools in the development of pick and place solutions for harvesting produce. Key technologies driving this include Internet of Things (IoT), machine learning (ML), deep learning (DL), robotics, additive manufacturing (AM), and simulation. Robots are often utilized as the main mechanism for harvesting operations. AM rapid prototyping strategies assist with designing specialty end-effectors and grippers. ML and DL algorithms allow for real-time object and obstacle detection. A comprehensive review of the literature is presented with a summary of the recent state-of-the-art I4.0 solutions in agricultural harvesting and current challenges/barriers to I4.0 adoption and integration with CAD/CAM tools and processes. A framework has also been developed to facilitate future CAD/CAM research and development for agricultural harvesting in the era of I4.0.

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Recent Advances in Intelligent Harvesting Robots

Yun,

An,

et al. 2024

Smart Agriculture

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Vision-based Vineyard Trunk Detection and its Integration into a Grapes Harvesting Robot

Cited by 10 publications

References 21 publications

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)

Leveraging I4.0 smart methodologies for developing solutions for harvesting produce

Recent Advances in Intelligent Harvesting Robots

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