Ramie Yield Estimation Based on UAV RGB Images

Huang, Fang; Wang, Chufeng; Cui, Guoxian; She, Wei; Zhao, Liang

doi:10.3390/s21020669

Cited by 17 publications

(7 citation statements)

References 33 publications

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“…Therefore, many researchers tend to use consumer-level sensors, but most of the previous studies were based on a single sensor data to extract plant height [ 30 , 32 ]. In this study, 2D-RGB, 2D-MS and 3D-RGB UAV imagery were collected respectively based on RGB camera and multispectral sensors, and it was found that the average plant height values of each plot extracted from these UAV imagery was lower than the ground measured values, which was consistent with the previous studies [ 48 , 49 ]. In view of the fact faba bean has branches, easy to lodging, and the plant height performance of each branch is quite different.…”

Section: Discussionsupporting

confidence: 89%

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

Chen

Qian

et al. 2022

Plant Methods

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Background Faba bean is an important legume crop in the world. Plant height and yield are important traits for crop improvement. The traditional plant height and yield measurement are labor intensive and time consuming. Therefore, it is essential to estimate these two parameters rapidly and efficiently. The purpose of this study was to provide an alternative way to accurately identify and evaluate faba bean germplasm and breeding materials. Results The results showed that 80% of the maximum plant height extracted from two-dimensional red–green–blue (2D-RGB) images had the best fitting degree with the ground measured values, with the coefficient of determination (R2), root-mean-square error (RMSE), and normalized root-mean-square error (NRMSE) were 0.9915, 1.4411 cm and 5.02%, respectively. In terms of yield estimation, support vector machines (SVM) showed the best performance (R2 = 0.7238, RMSE = 823.54 kg ha−1, NRMSE = 18.38%), followed by random forests (RF) and decision trees (DT). Conclusion The results of this study indicated that it is feasible to monitor the plant height of faba bean during the whole growth period based on UAV imagery. Furthermore, the machine learning algorithms can estimate the yield of faba bean reasonably with the multiple time points data of plant height.

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Section: Discussionsupporting

confidence: 89%

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

Chen

Qian

et al. 2022

Plant Methods

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“…These alternative methods are usually used when the fruit is hardly distinguished from the tree or increases the efficiency of the estimation. For example, the canopy projected area and canopy perimeter are correlated with the fruit load of peach trees 24 the individual crown area of the olive or almond can be used to predict yield 25 , 26 . The other one is directly counting the fruit from the tree.…”

Section: Introductionmentioning

confidence: 99%

Enhancing citrus fruit yield investigations through flight height optimization with UAV imaging

Kwon,

Ku,

et al. 2024

Sci Rep

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Citrus fruit yield is essential for market stability, as it allows businesses to plan for production and distribution. However, yield estimation is a complex and time-consuming process that often requires a large number of field samples to ensure representativeness. To address this challenge, we investigated the optimal altitude for unmanned aerial vehicle (UAV) imaging to estimate the yield of Citrus unshiu fruit. We captured images from five different altitudes (30 m, 50 m, 70 m, 90 m, and 110 m), and determined that a resolution of approximately 5 pixels/cm is necessary for reliable estimation of fruit size based on the average diameter of C. unshiu fruit (46.7 mm). Additionally, we found that histogram equalization of the images improved fruit count estimation compared to using untreated images. At the images from 30 m height, the normal image estimates fruit numbers as 73, 55, and 88. However, the histogram equalized image estimates 88, 71, 105. The actual number of fruits is 124, 88, and 141. Using a Vegetation Index such as IPCA showed a similar estimation value to histogram equalization, but I1 estimation represents a gap to actual yields. Our results provide a valuable database for future UAV field investigations of citrus fruit yield. Using flying platforms like UAVs can provide a step towards adopting this sort of model spanning ever greater regions at a cheap cost, with this system generating accurate results in this manner.

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“…Estimation (or prediction) of the plant yield can be based on the more complex analysis of color images. Particularly, plant quantity, plant height, and color parameters (G, B, R/B, (G-B)/(R-G), VARI, and GLI) can be used as input values for multiple and stepwise regression models to estimate plant yield [146]. The application of color indices and texture parameters as input for the RFE_ELM model can be effectively used to estimate cotton yield [147].…”

Section: For Detail)mentioning

confidence: 99%

RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review

Kior,

Yudina,

Zolin

et al. 2024

Plants

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Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary.

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Ramie Yield Estimation Based on UAV RGB Images

Cited by 17 publications

References 33 publications

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

Enhancing citrus fruit yield investigations through flight height optimization with UAV imaging

RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review

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