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 14 publications

(12 citation statements)

References 29 publications

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“…Photogrammetric imaging with unmanned aerial vehicles (UAVs) utilizes different types of multispectral or hyperspectral cameras [11][12][13][14][15][16]. The problem of crop yield prediction from photogrammetric data acquired by UAVs has been addressed in the literature [11].…”

Section: Field-wide Image Data Capturingmentioning

confidence: 99%

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

et al. 2023

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Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.

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Section: Field-wide Image Data Capturingmentioning

confidence: 99%

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

et al. 2023

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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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“…Desai et al [11] (2021) proposed a novel method for detecting maize crop tassels using k-means clustering and adaptive thresholding, demonstrating its performance closeness to reference methods through qualitative and quantitative analysis. Achieving precision of counting at the plot level.…”

Section: Introduction 21 Research Work By Relevant Scholarsmentioning

confidence: 99%

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

Niu,

Nie,

et al. 2024

Drones

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In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, and addressing issues such as pests, diseases, and nutrient deficiencies promptly. This ultimately ensures robust and high-yielding corn growth. This study introduces a method for the recognition and counting of corn tassels, using RGB imagery captured by unmanned aerial vehicles (UAVs) and the YOLOv8 model. The model incorporates the Pconv local convolution module, enabling a lightweight design and rapid detection speed. The ACmix module is added to the backbone section to improve feature extraction capabilities for corn tassels. Moreover, the CTAM module is integrated into the neck section to enhance semantic information exchange between channels, allowing for precise and efficient positioning of corn tassels. To optimize the learning rate strategy, the sparrow search algorithm (SSA) is utilized. Significant improvements in recognition accuracy, detection efficiency, and robustness are observed across various UAV flight altitudes. Experimental results show that, compared to the original YOLOv8 model, the proposed model exhibits an increase in accuracy of 3.27 percentage points to 97.59% and an increase in recall of 2.85 percentage points to 94.40% at a height of 5 m. Furthermore, the model optimizes frames per second (FPS), parameters (params), and GFLOPs (giga floating point operations per second) by 7.12%, 11.5%, and 8.94%, respectively, achieving values of 40.62 FPS, 14.62 MB, and 11.21 GFLOPs. At heights of 10, 15, and 20 m, the model maintains stable accuracies of 90.36%, 88.34%, and 84.32%, respectively. This study offers technical support for the automated detection of corn tassels, advancing the intelligence and precision of agricultural production and significantly contributing to the development of modern agricultural technology.

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Efficient Maize Tassel-Detection Method using UAV based remote sensing

Cited by 14 publications

References 29 publications

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

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

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

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