Estimating economic benefit of sugar beet based on three-dimensional computer vision: a case study in Inner Mongolia, China

Xiao, Shunfu; Chai, Honghong; Wang, Qing; Shao, Kwang‐Tsao; Meng, Lei; Wang, Ruili; Li, Baoguo; Ma, Yuntao

doi:10.1016/j.eja.2021.126378

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…The root yield and quality of sugar beet increased at the second TD compared with the first TD, possibly due to the suitability of micro‐climate factors such as temperature (Tmax and Tmin of TD 30‐April : 22.6 and 8.9°C; Tmax and Tmin of TD 13‐May : 27.4 and 11.5°C) and day length (TD 30‐April : 9.3 h day −1 ; TD 13‐May : 10.1 h day −1 ) during the critical period of sugar beet growth, leading to improved root development and sugar accumulation (July–September) (Lebedeva et al., 2020; Xiao et al., 2021). Hoffmann (2019) reported the difference between day and night temperature, 10 to 15°C, and low night temperatures are also beneficial factors in achieving maximum root and sugar yield.…”

Section: Discussionmentioning

confidence: 99%

Improving sugar beet yield, quality, and water use efficiency by nursery and transplanting practice under semi‐arid conditions

et al. 2023

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Transplanting is a useful technique to produce sugar beet (Beta vulgaris L.) in arid and semi‐arid regions that face water scarcity and salinity. The major challenge in transplanting is to improve the economic benefits that smallholder farmers have in the self‐production of transplants. The objective of this study was to evaluate the yield, quality, and water use efficiency (WUE) of sugar beet in response to transplanting and different options in transplant production process. Two field experiments were conducted at two sites. The first experiment investigated the effects of seedling age at transplanting, volume of the substrate, and transplanting date. The second experiment investigated the effects of substrate media and transplant production conditions on sugar beet yield and quality. In both experiments, transplanting was compared to the direct seeding on the conventional planting date at both sites. The highest white sugar content (12.1% and 13.5%) and white sugar yield (10.2 and 12.1 t ha−1) were obtained from plants transplanted in mid‐May at sites 1 and 2, respectively. Also, root yield, total sugar content, and WUE were higher in transplanting than direct seeding. The 40‐day‐old transplants produced in 22 mL cell volume when transferred to the field in mid‐May had higher WUE than the direct seeding. The transplants produced outside the greenhouse (uncontrolled conditions) had nearly the same yields as those produced inside a greenhouse under controlled conditions. We suggest that using readily available substrates and not requiring greenhouse conditions for transplant production is a cost‐effective way for smallholder farmers to produce their own seedlings.

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Section: Discussionmentioning

confidence: 99%

Improving sugar beet yield, quality, and water use efficiency by nursery and transplanting practice under semi‐arid conditions

et al. 2023

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“…Motion based structure combined with multiview stereo method can extract plant phenotypic features and estimate the economic benefits of crops efficiently and timely. Xiao et al [32] developed a framework for obtaining phenotypic traits based on the calculation of non-linear formulas and partial least squares regression models to estimate the economic benefits of various genotypes of crops. Specifically, the author designs a low-cost portable device for acquiring multiview images of crops to facilitate subsequent three-dimensional reconstruction.…”

Section: Computer-vision-based Economic Situation Predictionmentioning

confidence: 99%

Digital Economy Meets Artificial Intelligence: Forecasting Economic Conditions Based on Big Data Analytics

Wang

Zhao

2022

Mobile Information Systems

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Big data economy meets artificial intelligence, making the traditional statistical economy gradually evolve into an intelligent economy. Limited by human consciousness, traditional economic models have low prediction accuracy. In traditional statistical methods, the limited sample data also makes it impossible to effectively control and comprehensively forecast macroeconomic and development trends. Data economy has fundamentally transformed the traditional means of economic analysis. This is because the digital economy enables economic connectivity and precise data sharing, which can be used for precise economic statistics and mathematical analysis. Meanwhile, in terms of statistical methods, artificial intelligence methods no longer rely on human consciousness, but more objectively pay attention to economic cause and effect and are more accurate and comprehensive. This paper proposes an economic forecasting method based on artificial intelligence methods combined with big data analytics. In our model, we consider the economic statistics, equilibrium, and future prediction with the big data. Through the artificial intelligence method based on deep learning, the possible political factors, human activity factors, and social environmental factors in actual economic activities are effectively combined to form the main analysis subject affecting the economy. The results show that our model can be used as a basic model for economic statistics, economic analysis, economic decision-making, economic self-regulation, and other functions under the current development trend of the data economy.

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“…The results show that the combination of SFM and the multiple-view stereo (MVS) algorithm can generate a dense point cloud that contain more 3D information [6]. Currently, this technology has been used to reconstruct crop canopy spatial structure and plant organs in greenhouse, such as 3D modeling and analysis of leaves and fruits for cucumber (Cucumis sativus L.), eggplant (Solanum melongena L.), green pepper (Capsicum annuum L.), tomato (Solanum lycopersicum L.) and sugar beet (Beta vulgaris L.) [7][8][9][10]. Although this technology has been used to acquire information of individual plants and canopy information in the field [11][12][13], there are still few reports for the tall, complex branching, lush foliage and evergreen fruit trees in the 3D scale.…”

Section: Introductionmentioning

confidence: 99%

Estimating the light distribution within Chinese bayberry tree canopy based on a three-dimensional structural model

Li,

Zhang,

Hua

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Light distribution within fruit tree canopy is one of the important factors to optimize fruit tree architecture and to improve the potential production and fruit quality. Take the canopy structure, radiation environment and light transmission into consideration, plant model simulation analysis is an important way to measure light distribution within tree canopy in the field. There were 700 to 900 multi-view visible images for one Chinese bayberry tree obtained by moving a camera around the tree canopy. Using machine vision method, three-dimensional point cloud was reconstructed from images based on the multiple-view stereo and Structure From Motion (MVS-SFM) algorithm. The encapsulating, smoothing and filling algorithm were used to transform point clouds into smooth surfaces consisting of triangular facets in computers automatically. Canopy height, canopy width, branch length and circumference for the first three grades were measured in the orchard to evaluate the performance of the 3D structural model. Ray-tracing method in a 3D radiation model was used to calculate light distribution within the 3D tree canopy. The light distribution result within the 3D structural model was visualized using computer technology. The results showed that the multi-view image method well reconstructed the tree canopy. There was satisfactory correspondence between acquired and measured tree trunk height, circumference and canopy width, with the root mean squared error (RMSE) smaller than 1.2 cm and the consistency coefficient (d) larger than 0.90. The light was mainly distributed in the periphery of the canopy and in the upper-middle layer of tree canopy. Due to the sever occlusion of foliage and branches, the light was less in the inner and lower layer of the canopy. The fraction of light interception was 0.10, 0.17 and 0.05 for the bottom, middle and upper 1/3 canopy layer respectively. This study provide possibility for high-throughput 3D canopy structure measurement in the orchard and provide a reference for tree pruning, ideal tree structure design, and fruit yield and quality improvement.

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Estimating economic benefit of sugar beet based on three-dimensional computer vision: a case study in Inner Mongolia, China

Cited by 12 publications

References 43 publications

Improving sugar beet yield, quality, and water use efficiency by nursery and transplanting practice under semi‐arid conditions

Improving sugar beet yield, quality, and water use efficiency by nursery and transplanting practice under semi‐arid conditions

Digital Economy Meets Artificial Intelligence: Forecasting Economic Conditions Based on Big Data Analytics

Estimating the light distribution within Chinese bayberry tree canopy based on a three-dimensional structural model

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