Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

Ge, Haixiao; Ma, Fang; Li, Zhenwang; Du, Changwen

doi:10.3390/agronomy11122439

Cited by 16 publications

(6 citation statements)

References 67 publications

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“…Based on multi-temporal remote sensing data, a machine learning framework for cotton yield estimation is developed. In [34], Ge et al established a rice breeding yield estimation model using RGB images based on drones. The DJI Phantom 4 Pro UAV (DJI Technology Co., Ltd., Shenzhen, China) equipped with a 1-in CMOS 20-megapixel camera was used to collect rice data.…”

Section: Rgb Sensormentioning

confidence: 99%

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

Zhang,

Yu,

Zhang

et al. 2024

Agronomy

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With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management.

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Section: Rgb Sensormentioning

confidence: 99%

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

Zhang,

Yu,

Zhang

et al. 2024

Agronomy

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“…Remote sensing methods are used to map vegetation in the agricultural sector [14]. In recent years, unmanned aerial vehicles (UAVs) have attracted considerable interest in solving problems in the breeding process [15][16][17]. In particular, UAVs with optical suspension equipment have become useful tools in the evaluation of breeding plots [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

et al. 2023

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The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges’ rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties.

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“…Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. An accurate estimation of the physical traits will increase the effectiveness of rice breeding ( Ge et al, 2021 ; Luo et al, 2019 ). However, the conventional manual measurement of these traits is time-consuming and prone to fatigue, resulting in large errors in the results.…”

Section: Introductionmentioning

confidence: 99%

Size measurement and filled/unfilled detection of rice grains using backlight image processing

Feng,

Wang,

Zeng

et al. 2023

Front. Plant Sci.

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Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.

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Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

Cited by 16 publications

References 67 publications

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

Size measurement and filled/unfilled detection of rice grains using backlight image processing

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