Estimation of Canopy Area of Fruit Trees Using Light Unmanned Aerial Vehicle (UAV) and Image Processing Methods

Yıldız, Adil Koray; Keleş, Hakan; Aras, Servet

doi:10.24925/turjaf.v8i5.1039-1042.3164

Cited by 2 publications

(2 citation statements)

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“…The 𝜒, 𝛿 calculated using the two represent the accuracy and recall of the calculated results [23]. These two parameters can to some extent reflect the target detection ability of CBAMYOLOv5-CNN in ARSIs, but their shortcomings lie in not considering the impact of mean accuracy [24]. For this issue, the study uses Eq.…”

Section: Fig 5 Screening Process Of Redundant Information In Convolut...mentioning

confidence: 99%

Lightweight small target detection based on aerial remote sensing images

2024

J. meas. eng.

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With the upgrading of aviation space technology, the amount of information contained in remote sensing images in the aviation is gradually increasing, and the detection technology based on small targets has developed. For lightweight small targets, pixels per unit area contain more information than large targets, and their area is too small, which is easily overlooked by conventional detection models. To enhance the attention of such algorithms, this study first introduces a Control Bus Attention Mechanism (CBAM) in the fifth generation You Only Look Once (YOLOv5) algorithm to increase the algorithm’s attention to small targets and generate optimization algorithms. Then convolutional neural network is used to mark feature pixels of the optimization algorithm, eliminate redundant information, and generate fusion algorithm, which is used to generate redundant information with high similarity when the optimization algorithm surveys pixel blocks. The novelty of this study lies in using CBAM to improve YOLOv5 algorithm. CBAM module can extract important features from images by adaptively learning the channel and spatial attention of feature maps. By weighting the channel and spatial attention of the feature map, the network can pay more attention to important features and suppress irrelevant background information. This attention mechanism can help the network better capture the characteristics of small targets and improve the accuracy and robustness of detection. Embedding CBAM module into YOLOv5 detection network can enhance the network's perception of small targets. CBAM module can improve the expressive ability and feature extraction ability of the network without increasing the complexity of the network. By introducing CBAM module, YOLOv5 can better capture the characteristics of small targets in aerial remote sensing images, and improve the detection accuracy and recall rate. Finally, the proposed fusion algorithm is used for experiments on the Tiny-Person dataset and compared with the fifth, sixth, and seventh generations of You Only Look Once. When the fusion algorithm tests the target, the classification accuracy of Sea-person is 39 %, the classification accuracy of Earth-person is 31 %, and the probability of being predicted as the background is 56 % and 67 %, respectively. And the overall accuracy of this algorithm is 0.987, which is the best among the four algorithms. The experimental results show that the fusion algorithm proposed in the study has precise positioning for lightweight small targets and can achieve good application results in aerial remote sensing images.

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Section: Fig 5 Screening Process Of Redundant Information In Convolut...mentioning

confidence: 99%

Lightweight small target detection based on aerial remote sensing images

2024

J. meas. eng.

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“…When focusing on RGB images, a large number of studies of tree phenotype in orchards can be found. Using image processing techniques, Yıldız et al (2020) determined the canopy area of apple trees. Regression analysis employed both circular and elliptical calculating techniques.…”

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confidence: 99%

Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning

Zhang

Chen

et al. 2022

Front. Plant Sci.

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The widespread use of unmanned aerial vehicles (UAV) is significant for the effective management of orchards in the context of precision agriculture. To reduce the traditional mode of continuous spraying, variable target spraying machines require detailed information about tree canopy. Although deep learning methods have been widely used in the fields of identifying individual trees, there are still phenomena of branches extending and shadows preventing segmenting edges of tree canopy precisely. Hence, a methodology (MPAPR R-CNN) for the high-precision segment method of apple trees in high-density cultivation orchards by low-altitude visible light images captured is proposed. Mask R-CNN with a path augmentation feature pyramid network (PAFPN) and PointRend algorithm was used as the base segmentation algorithm to output the precise boundaries of the apple tree canopy, which addresses the over- and under-sampling issues encountered in the pixel labeling tasks. The proposed method was tested on another miniature map of the orchard. The average precision (AP) was selected to evaluate the metric of the proposed model. The results showed that with the help of training with the PAFPN and PointRend backbone head that AP_seg and AP_box score improved by 8.96% and 8.37%, respectively. It can be concluded that our algorithm could better capture features of the canopy edges, it could improve the accuracy of the edges of canopy segmentation results.

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Estimation of Canopy Area of Fruit Trees Using Light Unmanned Aerial Vehicle (UAV) and Image Processing Methods

Cited by 2 publications

References 0 publications

Lightweight small target detection based on aerial remote sensing images

Lightweight small target detection based on aerial remote sensing images

Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning

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