Efficient Maize Tassel-Detection Method using UAV based remote sensing

Kumar, Ajay; Desai, Sai Vikas; Balasubramanian, Vineeth N; Rajalakshmi, P.; Guo, Wei; Naik, B. Balaji; Marathi, Balram; Desai, U.B.

doi:10.1016/j.rsase.2021.100549

Cited by 13 publications

(9 citation statements)

References 29 publications

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“…These above challenges force us to propose a more reliable and efficient sample labeling method, so we released the dataset used in this study, hoping to reduce workload of researchers engaged in similar work. In the future, we will explore unsupervised method combined with manual verification to generate a large number of samples [17] and constantly update this dataset.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, UAVs equipped with various sensors has been regarded as an effective tool for crop science [9][10][11][12][13] due to its advantages of low construction cost, flexibility and high spatio-temporal resolution [14][15][16]. At present, researches on maize tassel detection is mainly include classical machine learning based on multi-step combination, end-to-end objective detection algorithm and semisupervised [17] or unsupervised methods [18] to solve large sample dependence.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of key factors affecting maize tassels detection and construction of shared dataset based on UAV

Zan

Gao

Liu

et al. 2023

Preprint

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Background Rapid and accurate detection of tassels is of great significance for maize breeding, seed production and the acquisition of key growth stage. To liberate manpower and improve the efficiency of production management, many automatic detection methods with acceptable accuracy have been proposed. However, images acquisition parameters of these methods were quite different, so they cannot provide an operable standard for practical applications. In this study, based on multi-temporal unmanned aerial vehicle (UAV) RGB images with maize flowering stage, we created UAV Maize Tassel Detection (UAVMTD) dataset, and used Faster R-CNN to answer what are the key factors affecting detection accuracy from two aspects of efficient use of samples and data acquisition standards. Based on the detection results, we estimated tasseling date of different plots and analyzed varieties’ differences. Results The results show that model performance would not be greatly affected before the amount of training data changed by orders of magnitude, but it can be improved effectively by adjusting sub-images’ sizes, and the final model was selected with AP@0.5IOU was 0.916; images obtained at 12 pm were more suitable for tassels detection, AP@0.5IOU, recall and precision were 3%, 2% and 6% higher than that at 8 am; optimal spatial resolution was around 1cm for tassels detection by considering the recognition effect and data acquisition efficiency. Conclusions This study analyzed key factors affecting maize tassels detection and provided a reasonable reference for future applications, which is helpful to screen out varieties from large-scale breeding materials.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of key factors affecting maize tassels detection and construction of shared dataset based on UAV

Zan

Gao

Liu

et al. 2023

Preprint

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“…Maize (Zea mays L.) plants of the same cultivar were grown on 30 subplots in the field in 2018 (Figure S1a) and 2019 (Figure S1b) in India (N17 • 19 27.22", E78 • 23 55.71"), see [36] for details. Three different irrigation levels were set, i.e., 60% (level 1), 80% (level 2) and 120% (level 3) of cumulative pan evaporation were applied at the subplot level throughout the growing season.…”

Section: Uav Image Acquisition Of Msi and Ncrgb Images From Maize And...mentioning

confidence: 99%

Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping

et al. 2022

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Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture.

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“…Recently, the rapid development of unmanned aerial vehicle (UAV) technology provides a new opportunity for continuous acquisition of rapeseed data under different growing stages and it supports different image resolutions. The flexibility and convenience establish an easy way to monitor flowering crops (Kumar et al, 2021;Xu et al, 2021). Wan et al (2018) employed Red-Green-Blue (RGB) and multispectral images to establish a model to estimate yellow flower number.…”

Section: Introductionmentioning

confidence: 99%

“…More researchers deal with counting as an object detection task, in which target quantity could be estimated from the number of detected bounding boxes. These methods are proven to outperform some traditional machine learning models in counting of maize ( Kumar et al., 2021 ), cotton bloom ( Xu et al., 2018 ), sorghum heads and wheat ears ( Lu and Cao, 2020 ; Lu et al., 2022 ), etc. Nevertheless, only little attention pays on automatic counting of the rapeseed inflorescences using UAV-RGB because counting rapeseed inflorescences is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

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

Li²,

Qiao³

et al. 2023

Front. Plant Sci.

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Flowering is a crucial developing stage for rapeseed (Brassica napus L.) plants. Flowers develop on the main and branch inflorescences of rapeseed plants and then grow into siliques. The seed yield of rapeseed heavily depends on the total flower numbers per area throughout the whole flowering period. The number of rapeseed inflorescences can reflect the richness of rapeseed flowers and provide useful information for yield prediction. To count rapeseed inflorescences automatically, we transferred the counting problem to a detection task. Then, we developed a low-cost approach for counting rapeseed inflorescences using YOLOv5 with the Convolutional Block Attention Module (CBAM) based on unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) imagery. Moreover, we constructed a Rapeseed Inflorescence Benchmark (RIB) to verify the effectiveness of our model. The RIB dataset captured by DJI Phantom 4 Pro V2.0, including 165 plot images and 60,000 manual labels, is to be released. Experimental results showed that indicators R2 for counting and the mean Average Precision (mAP) for location were over 0.96 and 92%, respectively. Compared with Faster R-CNN, YOLOv4, CenterNet, and TasselNetV2+, the proposed method achieved state-of-the-art counting performance on RIB and had advantages in location accuracy. The counting results revealed a quantitative dynamic change in the number of rapeseed inflorescences in the time dimension. Furthermore, a significant positive correlation between the actual crop yield and the automatically obtained rapeseed inflorescence total number on a field plot level was identified. Thus, a set of UAV- assisted methods for better determination of the flower richness was developed, which can greatly support the breeding of high-yield rapeseed varieties.

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Efficient Maize Tassel-Detection Method using UAV based remote sensing

Cited by 13 publications

References 29 publications

Analysis of key factors affecting maize tassels detection and construction of shared dataset based on UAV

Analysis of key factors affecting maize tassels detection and construction of shared dataset based on UAV

Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping

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

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