Applications of machine vision in agricultural robot navigation: A review

Wang, Tianhai; Chen, Bin; Zhang, Zhenqian; Li, Han; Zhang, Man

doi:10.1016/j.compag.2022.107085

Cited by 128 publications

(42 citation statements)

References 72 publications

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“…Outdoor and urban navigation for people with visual impairments [11]. 2022 Visual Navigation Agricultural robot navigation [12]. 2022 Spacecraft Navigation Deep learning for spacecraft dynamics control, guidance and navigation [40].…”

Section: Year Topic Highlightmentioning

confidence: 99%

“…Numerous surveys have been conducted on the applications of deep learning in various navigation domains, including urban navigation [11], visual navigation [12,13], reinforcement learning [14,9], obstacle detection [15], and spacecraft navigation [16,17]. However, there is a lack of comprehensive surveys that provide a general overview of the use of deep learning in navigation.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

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Section: Year Topic Highlightmentioning

confidence: 99%

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confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

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“…Stateoftheart progress in research and future challenges is docu mented in a wide range of review papers and book chapters addressing agriculture in general [1], [3], [4], [5] and specific application domains, including phenotyping [6], [7], [8] ara ble farming [9], livestock farming [10], greenhouse horticulture [2], orchard management [11], forestry [12], and food processing [13]. Review papers also address specific technologies in the context of agricultural robotics, such as computer vision [14], [15], active perception [16], unmanned aer ial vehicle technologies [17], [18], cov erage path planning in arable farming [19], and grasping and soft grasping [20], [21]. Some illustrative examples of agrifood robotics are documented in Figure 2.…”

Section: Challengesmentioning

confidence: 99%

Agricultural Robotics and Automation [TC Spotlight]

Henten¹,

Tabb²,

Billingsley³

et al. 2022

IEEE Robot. Automat. Mag.

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“…Deep learning can solve various problems in precision agriculture with the development of various systems (Solemane et al, 2022). A powerful technical tool in artificial intelligence, computer vision (Wang et al, 2022) has provided a strong technical guarantee in the vision system of agricultural robots. Agricultural robots (Nguyen et al, 2021) can help farmers to solve farming, pesticide, and picking problems in an environmentally friendly, energy-saving, and cost-saving way to improve agricultural production efficiency and increase income.…”

Section: Introductionmentioning

confidence: 99%

Assessing Pineapple Maturity in Complex Scenarios Using an Improved Retinanet Algorithm

2023

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In China, low levels of accuracy in predicting when pineapple crops will reach maturity can result from environmental variation such as light changes, fruit overlap, and shading. Therefore, this study proposed the use of an improved RetinaNet algorithm (ECA-Retinanet) based on the ECA attention mechanism. The ECA attention mechanism was embedded into the classification subnet of RetinaNet to improve accuracy in detecting different levels of maturity in pineapples. A new pineapple dataset was collected comprising four different growth stages under mild and severe complex scenarios. The experimental results have shown that the mAP (Mean Average Precision) and F1 score (Balanced Score) of the ECA-Retinanet model were 97.69%, 94.75%, 93.2%, and 90% for identification in mild and severe complex scenarios. These values are 0.42%, 2%, 1.78%, and 1.5% higher than the original RetinaNet model which exceeds those of the six existing state-of-the-art detection models. The results have indicated that the proposed algorithm could be used for accurate identification of pineapple fruit and can detect fruit maturity using ground color images in the natural environment. The study findings provide a technical reference for automatic picking robots and early yield estimation.

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Applications of machine vision in agricultural robot navigation: A review

Cited by 128 publications

References 72 publications

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Agricultural Robotics and Automation [TC Spotlight]

Assessing Pineapple Maturity in Complex Scenarios Using an Improved Retinanet Algorithm

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