ArticleFrontiers in plant science2022
A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition.Frontiers in plant science · 2025Article
- Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.Frontiers in plant science · 2025Article
- Detection of breakage and impurity ratios for raw sugarcane based on estimation model and MDSC-DeepLabv3.Frontiers in plant science · 2023Article
- Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review.Frontiers in plant science · 2023Review
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11 authors.
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Abstract
Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets
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