Fast detection and location of longan fruits using UAV images

Li, Denghui; Sun, Xiaoxuan; Elkhouchlaa, Hamza; Jia, Yuhang; Yao, Zhongwei; Lin, Pei-Yi; Li, Jun; Lu, Huazhong

doi:10.1016/j.compag.2021.106465

Cited by 32 publications

(20 citation statements)

References 38 publications

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“…The unmanned aerial systems can also be expensive, but there are models that are inexpensive. The disadvantages of unmanned aerial systems are the requirement of a human operator [49], limited flight duration, the limited weight of cargo carried, energy consumption [41], and dependence on weather conditions [41,52]. In addition, the processing of data collected by UAS is higher than that of satellite imagery [49].…”

Section: Data Acquired and Data Preparationmentioning

confidence: 99%

“…Li et al [52] used a deep-learning algorithm to quickly and accurately detect and locate longan fruit in imagery and pick the fruit with a UAV device. An RGB-D camera on the UAV was used to collect images of the fruits.…”

Section: Automation In Agriculturementioning

confidence: 99%

“…Moreover, implementation on devices such as smartphones still, brings various challenges that need to be considered, such as memory consumption, speed, etc. Single-stage detection models such as YOLO and SSD, lightweight classification models such as MobileNet, the development of edge devices such as Raspberry Pi and Jetson nano, and the use of cloud computing have made it possible to deploy Deep Learning models in real-world applications and practical ways [34,52,72,86,109]. The quantization method can be also used to compress the model size and improve the detection speed [82].…”

Section: Disease Detectionmentioning

confidence: 99%

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A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

et al. 2022

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Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.

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Section: Data Acquired and Data Preparationmentioning

confidence: 99%

Section: Automation In Agriculturementioning

confidence: 99%

Section: Disease Detectionmentioning

confidence: 99%

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A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

et al. 2022

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“…In contrast, developing nations in Africa have a critical mass of unemployed youth [ 44 , 45 , 46 , 47 , 48 ]. Drawing from the employment dynamics in the global north and south, labor shortage is not a sufficient incentive for the transition to soft robots and robot insects.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture

et al. 2022

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Biomimetics is the interdisciplinary cooperation of biology and technology that offers solutions to practical problems by analyzing biological systems and transferring their principles into applications. This review article focused on biomimetic innovations, including bio-inspired soft robots and swarm robots that could serve multiple functions, including the harvesting of fruits, pest control, and crop management. The research demonstrated commercially available biomimetic innovations, including robot bees by Arugga AI Farming and the Robotriks Traction Unit (RTU) precision farming equipment. Additionally, soft robotic systems have made it possible to mitigate the risk of surface bruises, rupture, the crushing destruction of plant tissue, and plastic deformation in the harvesting of fruits with a soft rind such as apples, cherries, pears, stone fruits, kiwifruit, mandarins, cucumbers, peaches, and pome. Even though the smart farming technologies, which were developed to mimic nature, could help prevent climate change and enhance the intensification of agriculture, there are concerns about long-term ecological impact, cost, and their inability to complement natural processes such as pollination. Despite the problems, the market for bio-inspired technologies with potential agricultural applications to modernize farming and solve the abovementioned challenges has increased exponentially. Future research and development should lead to low-cost FEA robotic grippers and FEA-tendon-driven grippers for crop harvesting. In brief, soft robots and swarm robotics have immense potential in agriculture.

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“…At present, the visual algorithm of environment perception can complete the task of environment recognition 42 , 48 – 60 . By combining LBS positioning and other methods, the environmental information can be preset in advance, and the recognition speed and accuracy of the visual algorithm can be improved at the same time.…”

Section: Introductionmentioning

confidence: 99%

Study on a risk model for prediction and avoidance of unmanned environmental hazard

Qiu

Zhang

et al. 2022

Sci Rep

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Comprehensive research is conducted on the design and control of the unmanned systems for electric vehicles. The environmental risk prediction and avoidance system is divided into the prediction part and the avoidance part. The prediction part is divided into environmental perception, environmental risk assessment, and risk prediction. In the avoidance part, according to the risk prediction results, a conservative driving strategy based on speed limit is adopted. Additionally, the core function is achieved through the target detection technology based on deep learning algorithm and the data conclusion based on deep learning method. Moreover, the location of bounding box is further optimized to improve the accuracy of SSD target detection method based on solving the problem of imbalanced sample categories. Software such as MATLAB and CarSim are applied in the system. Bleu-1 was 67.1, bleu-2 was 45.1, bleu-3 was 29.9 and bleu-4 was 21.1. Experiments were carried out on the database flickr30k by designing the algorithm. Bleu-1 was 72.3, bleu-2 was 51.8, bleu-3 was 37.1 and bleu-4 was 25.1. From the comparison results of the simulations of unmanned vehicles with or without a system, it can provide effective safety guarantee for unmanned driving.

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Fast detection and location of longan fruits using UAV images

Cited by 32 publications

References 38 publications

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture

Study on a risk model for prediction and avoidance of unmanned environmental hazard

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