Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery

Li, Jie; Li, Yang; Qiao, Jiangwei; Li, Li; Wang, Xinfa; Yao, Jian; Liao, Guisheng

doi:10.3389/fpls.2023.1101143

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…The authors also reported difficulties occurring in training and detection due to multiple inflorescences overlapping as occlusions. This type of under-detection caused by inflorescence overlap was also reported by Li et al, (2023) [49] in the effort to count B. napus inflorescences using UAV-acquired imagery to train CenterNet CNN models.…”

Section: Statistical Precisionsupporting

confidence: 65%

Detection and Quantification of Arnica montana L. Inflorescences in Grassland Ecosystems Using Convolutional Neural Networks and Drone-Based Remote Sensing

Sângeorzan,

Păcurar,

Reif

et al. 2024

Remote Sensing

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Arnica montana L. is a medicinal plant with significant conservation importance. It is crucial to monitor this species, ensuring its sustainable harvesting and management. The aim of this study is to develop a practical system that can effectively detect A. montana inflorescences utilizing unmanned aerial vehicles (UAVs) with RGB sensors (red–green–blue, visible light) to improve the monitoring of A. montana habitats during the harvest season. From a methodological point of view, a model was developed based on a convolutional neural network (CNN) ResNet101 architecture. The trained model offers quantitative and qualitative assessments of A. montana inflorescences detected in semi-natural grasslands using low-resolution imagery, with a correctable error rate. The developed prototype is applicable in monitoring a larger area in a short time by flying at a higher altitude, implicitly capturing lower-resolution images. Despite the challenges posed by shadow effects, fluctuating ground sampling distance (GSD), and overlapping vegetation, this approach revealed encouraging outcomes, particularly when the GSD value was less than 0.45 cm. This research highlights the importance of low-resolution image clarity, on the training data by the phenophase, and of the need for training across different photoperiods to enhance model flexibility. This innovative approach provides guidelines for mission planning in support of reaching sustainable management goals. The robustness of the model can be attributed to the fact that it has been trained with real-world imagery of semi-natural grassland, making it practical for fieldwork with accessible portable devices. This study confirms the potential of ResNet CNN models to transfer learning to new plant communities, contributing to the broader effort of using high-resolution RGB sensors, UAVs, and machine-learning technologies for sustainable management and biodiversity conservation.

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Section: Statistical Precisionsupporting

confidence: 65%

Detection and Quantification of Arnica montana L. Inflorescences in Grassland Ecosystems Using Convolutional Neural Networks and Drone-Based Remote Sensing

Sângeorzan,

Păcurar,

Reif

et al. 2024

Remote Sensing

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“…Additionally, Chen and Liao [21] provided fast region-based convolutional neural network (Fast R-CNN) architecture to detect and classify palm trees. Li et al [22] developed a low-cost approach for counting rapeseed inflorescences using YOLOv5 with the convolutional block attention module based on UAV RGB imagery. A similar study was also conducted in the counting of rice seedlings and achieved a promised recognition accuracy [23].…”

Section: Introductionmentioning

confidence: 99%

Rubber Tree Recognition Based on UAV RGB Multi-Angle Imagery and Deep Learning

Liang,

Sun,

Kou

et al. 2023

Drones

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The rubber tree (Hevea brasiliensis) is an important tree species for the production of natural latex, which is an essential raw material for varieties of industrial and non-industrial products. Rapid and accurate identification of the number of rubber trees not only plays an important role in predicting biomass and yield but also is beneficial to estimating carbon sinks and promoting the sustainable development of rubber plantations. However, the existing recognition methods based on canopy characteristic segmentation are not suitable for detecting individual rubber trees due to their high canopy coverage and similar crown structure. Fortunately, rubber trees have a defoliation period of about 40 days, which makes their trunks clearly visible in high-resolution RGB images. Therefore, this study employed an unmanned aerial vehicle (UAV) equipped with an RGB camera to acquire high-resolution images of rubber plantations from three observation angles (−90°, −60°, 45°) and two flight directions (SN: perpendicular to the rubber planting row, and WE: parallel to rubber planting rows) during the deciduous period. Four convolutional neural networks (multi-scale attention network, MAnet; Unet++; Unet; pyramid scene parsing network, PSPnet) were utilized to explore observation angles and directions beneficial for rubber tree trunk identification and counting. The results indicate that Unet++ achieved the best recognition accuracy (precision = 0.979, recall = 0.919, F-measure = 94.7%) with an observation angle of −60° and flight mode of SN among the four deep learning algorithms. This research provides a new idea for tree trunk identification by multi-angle observation of forests in specific phenological periods.

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Automatic Identification of Sea Rice Grains in Complex Field Environment Based on Deep Learning

Deng,

Cheng,

Liu

et al. 2024

Agriculture

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The number of grains per sea rice panicle is an important parameter directly related to rice yield, and it is also a very important agronomic trait in research related to sea rice breeding. However, the grain number per sea rice panicle still mainly relies on manual calculation, which has the disadvantages of being time-consuming, error-prone, and labor-intensive. In this study, a novel method was developed for the automatic calculation of the grain number per rice panicle based on a deep convolutional neural network. Firstly, some sea rice panicle images were collected in complex field environment and annotated to establish the sea rice panicle image data set. Then, a sea grain detection model was developed using the Faster R-CNN embedded with a feature pyramid network (FPN) for grain identification and location. Also, ROI Align was used to replace ROI pooling to solve the problem of relatively large deviations in the prediction frame when the model detected small grains. Finally, the mAP (mean Average Precision) and accuracy of the sea grain detection model were 90.1% and 94.9%, demonstrating that the proposed method had high accuracy in identifying and locating sea grains. The sea rice grain detection model can quickly and accurately predict the number of grains per panicle, providing an effective, convenient, and low-cost tool for yield evaluation, crop breeding, and genetic research. It also has great potential in assisting phenotypic research.

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Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery

Cited by 3 publications

References 54 publications

Detection and Quantification of Arnica montana L. Inflorescences in Grassland Ecosystems Using Convolutional Neural Networks and Drone-Based Remote Sensing

Detection and Quantification of Arnica montana L. Inflorescences in Grassland Ecosystems Using Convolutional Neural Networks and Drone-Based Remote Sensing

Rubber Tree Recognition Based on UAV RGB Multi-Angle Imagery and Deep Learning

Automatic Identification of Sea Rice Grains in Complex Field Environment Based on Deep Learning

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