A novel approach for the 3D localization of branch picking points based on deep learning applied to longan harvesting UAVs

Li, Denghui; Sun, Xiaoxuan; Lv, Shengping; Elkhouchlaa, Hamza; Jia, Yuhang; Yao, Zhongwei; Lin, Pei-Yi; Zhou, Haobo; Zhou, Zhengqi; Shen, Jiaxing; Li, Jun

doi:10.1016/j.compag.2022.107191

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…To overcome these challenges, researchers are exploring various new methods and technologies. For example, some studies use deep learning techniques to improve the detection and matching performance of feature points [22][23][24]. Some studies also consider using multimodal sensor fusion to improve positioning accuracy and robustness [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

Geng,

Yang,

Liu

2024

JAI

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This article presents a real-time localization method for Unmanned Aerial Vehicles (UAVs) based on continuous image processing. The proposed method employs the Scale Invariant Feature Transform (SIFT) algorithm to identify key points in multi-scale space and generate descriptor vectors to match identical objects across multiple images. These corresponding points in the image provide pixel positions, which can be combined with transformation equations, allow for the calculation of the UAV's actual ground position. Additionally, the physical coordinates of matching points in the image can be obtained, corresponding to the UAV's physical coordinates. The method achieves real-time positioning and tracking during UAV flight, with experimental results demonstrating that within an acceptable error range, the UAV coordinates calculated using the proposed algorithm are consistent with the actual coordinates. The maximum error along the x-axis, y-axis, and z-axis is 4.501 cm, with the horizontal error exhibiting high stationarity and the vertical error having a low average value of 0.041 cm. The real-time positioning algorithm presented in this article possesses characteristics such as simplicity, ease of implementation, and low error, making it suitable for UAVs with limited computational processing power.

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Section: Related Workmentioning

confidence: 99%

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

Geng,

Yang,

Liu

2024

JAI

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“…PyTorch ▪ An open-source machine learning framework ▪ Based on Python programming language and Torch library https://pytorch.org/ (Cochero et al, 2022), (Du et al, 2022), (Dai et al, 2021), (Guo et al, 2021), (Huang et al, 2022), (Lv et al, 2022), , , (Zhang et al, 2023), (Shi et al, 2020) TensorFlow ▪ An end-to-end open-source machine learning platform https:// www.tensorflow.org/ (Ahmad et al, 2021), (Alsanea et al, 2022), (Bereciartua-Peŕez et al, 2022), (De Cesaro Juńior et al, 2022), , (Guo et al, 2021), (Hossain et al, 2019), (Karam et al, 2022), (Knyshov et al, 2021), (Rajeena et al, 2022), (Rimal et al, 2022), (Sharma et al, 2020), (Takimoto et al, 2021), (Valan et al, 2019), , , (Wu et al, 2019) Keras ▪ High-level, modular, and flexible open-source neural network library and API based on Python programming language https://keras.io/ (Ayan et al, 2020), , (Hossain et al, 2019), (Karam et al, 2022), (Knyshov et al, 2021), (Lu et 3 New trends in harmful insect and pest detection…”

Section: Software Description Link Papersmentioning

confidence: 99%

“…The tendency of researchers to modify the NN backbone was also observed. Modifying the backbone of a pre-trained NN for a given task is a typical approach in deep learning (Li Z. et al, 2022). Many cutting-edge models are constructed on modified backbones of pre-trained NNs (Kuzuhara et al, 2020;Liu et al, 2022).…”

Section: Software Description Link Papersmentioning

confidence: 99%

“…Harmful insect detection (Albanese et al, 2021), (Alsanea et al, 2022), (Ayan et al, 2020), (Butera et al, 2021), (Cochero et al, 2022), (Genaev et al, 2022), (Guo et al, 2021), (Hansen et al, 2019), (Hong et al, 2021), (Hossain et al, 2019), (Iost Filho et al, 2022), (Espinoza et al, 2016), (Kasinathan et al, 2021), (Khanramaki et al, 2021), (Knyshov et al, 2021), (Li et al, 2019), , , (Liu et al, 2019), (Liu and Wang, 2020), (Lv et al, 2022), (Malathi and Gopinath, 2021), (Nagar and Sharma, 2021), (Nanni et al, 2022), (Rajeena et al, 2022), (Rimal et al, 2022), (Rustia et al, 2020), (Sanghavi et al, 2022), (Teng et al, 2022), (Valan et al, 2019), , , (Xia et al, 2018), , (Shi et al, 2020) Infected crops by insects (Bereciartua-Peŕez et al, 2022), (Bhoi et al, 2021), , (Espinoza et al, 2016), (Kusrini et al, 2021), (Nazri et al, 2018), (Sharma et al, 2020), (Singh et al, 2021), (Tian G. et al, 2020), (Turkoglu et al, 2020), (Turkoglu et al, 2022), (Wu et al, 2019),…”

Section: Application Papersmentioning

confidence: 99%

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IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…In this paper, according to the growth characteristics of Cluster_Fruit, the improved YOLOv5s target detection algorithm ( Li et al., 2022 ) and Deepsort target tracking algorithm are incorporated into the CF-YD model. Figure 6A shows the flow chart for the numerical Cluster_Fruit statistics in a single fruit tree.…”

Section: Model Construction and Statistical Strategy For Fruit Tree Y...mentioning

confidence: 99%

A longan yield estimation approach based on UAV images and deep learning

Sun²,

Jia³

et al. 2023

Front. Plant Sci.

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Longan yield estimation is an important practice before longan harvests. Statistical longan yield data can provide an important reference for market pricing and improving harvest efficiency and can directly determine the economic benefits of longan orchards. At present, the statistical work concerning longan yields requires high labor costs. Aiming at the task of longan yield estimation, combined with deep learning and regression analysis technology, this study proposed a method to calculate longan yield in complex natural environment. First, a UAV was used to collect video images of a longan canopy at the mature stage. Second, the CF-YD model and SF-YD model were constructed to identify Cluster_Fruits and Single_Fruits, respectively, realizing the task of automatically identifying the number of targets directly from images. Finally, according to the sample data collected from real orchards, a regression analysis was carried out on the target quantity detected by the model and the real target quantity, and estimation models were constructed for determining the Cluster_Fruits on a single longan tree and the Single_Fruits on a single Cluster_Fruit. Then, an error analysis was conducted on the data obtained from the manual counting process and the estimation model, and the average error rate regarding the number of Cluster_Fruits was 2.66%, while the average error rate regarding the number of Single_Fruits was 2.99%. The results show that the method proposed in this paper is effective at estimating longan yields and can provide guidance for improving the efficiency of longan fruit harvests.

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A novel approach for the 3D localization of branch picking points based on deep learning applied to longan harvesting UAVs

Cited by 11 publications

References 37 publications

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

A longan yield estimation approach based on UAV images and deep learning

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