Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Xu, Jingxian; Ma, Jun; Tang, Yanan; Wu, Weixiong; Shao, Jin-Hua; Wu, Wanben; Wei, Shu-Yun; Liu, Yi-Fei; Wang, Yuan‐Chen; Guo, Haiqiang

doi:10.3390/rs12172823

Cited by 54 publications

(31 citation statements)

References 63 publications

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“…For some crops, there is a direct allometric relationship between the harvested component and the biomass of the whole crop, such that crop yield can be estimated by 'indirect' measurements of more easily assessed attributes. For example, sugarcane crop height and density are correlated to cane yield [61]. For tree fruit, a given tree will have a genetically and physiologically determined maximum fruit biomass that it will tend towards.…”

Section: What Determines Tree Fruit Load?mentioning

confidence: 99%

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Anderson

Walsh

Wulfsohn³

2021

Agronomy

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The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.

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Section: What Determines Tree Fruit Load?mentioning

confidence: 99%

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Anderson

Walsh

Wulfsohn³

2021

Agronomy

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“…Hence, gaps of 0.5-1.0 m are not likely for remote detection (Figure 5). Other factors limiting sensors to have access to gaps potentially include weed spread in regions of importance [29] and trampling by machineries [30]. However, they could not be sources of errors in our study.…”

Section: Discussionmentioning

confidence: 87%

Mapping Gaps in Sugarcane by UAV RGB Imagery: The Lower and Earlier the Flight, the More Accurate

et al. 2021

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Imagery data prove useful for mapping gaps in sugarcane. However, if the quality of data is poor or the moment of flying an aerial platform is not compatible to phenology, prediction becomes rather inaccurate. Therefore, we analyzed how the combination of pixel size (3.5, 6.0 and 8.2 cm) and height of plant (0.5, 0.9, 1.0, 1.2 and 1.7 m) could impact the mapping of gaps on unmanned aerial vehicle (UAV) RGB imagery. Both factors significantly influenced mapping. The larger the pixel or plant, the less accurate the prediction. Error was more likely to occur for regions on the field where actively growing vegetation overlapped at gaps of 0.5 m. Hence, even 3.5 cm pixel did not capture them. Overall, pixels of 3.5 cm and plants of 0.5 m outstripped other combinations, making it the most accurate (absolute error ~0.015 m) solution for remote mapping on the field. Our insights are timely and provide forward knowledge that is particularly relevant to progress in the field’s prominence of flying a UAV to map gaps. They will enable producers to make decisions on replanting and fertilizing site-specific high-resolution imagery data.

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“…Airborne LiDAR enables yield acquisition of agricultural products due to its convenience and high efficiency [61]. However, the quality of the acquired point clouds is limited in complex terrain and forest conditions [62].…”

Section: Point Cloud Acquisitionmentioning

confidence: 99%

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

et al. 2022

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Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management.

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Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Cited by 54 publications

References 63 publications

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Mapping Gaps in Sugarcane by UAV RGB Imagery: The Lower and Earlier the Flight, the More Accurate

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

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