In-Field Grape Cluster Size Assessment for Vine Yield Estimation Using a Mobile Robot and a Consumer Level RGB-D Camera

Kurtser, Polina; Ringdahl, Ola; Rotstein, Nati; Berenstein, Ron; Edan, Yael

doi:10.1109/lra.2020.2970654

Cited by 50 publications

(32 citation statements)

References 25 publications

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“…Ferencz et al [64] and Franch et al [65] monitored yield distribution using Landsat Thematic Mapper (TM) data and moderate Resolution Imaging Spectroradiometer (MODIS) data, respectively, both obtaining good results. Additionally, Kurtser et al [66] found that RGB camera data can be used to accurately estimate yield. In future research, we plan to use different remote sensing data for yield estimation and to explore the estimation effect of various sensors.…”

Section: Yield Estimation Using Partial Least Squares Regression Ranmentioning

confidence: 99%

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

Tao

Feng

et al. 2020

Sensors

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Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (HCSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and HCSM were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques. The three different regression techniques were used for modeling and verification to test the stability of the yield estimation. The results showed that: (1) HCSM is strongly correlated with H (R2 = 0.97); (2) of the regression techniques, the best yield prediction was obtained using PLSR, followed closely by ANN, while RF had the worst prediction performance; and (3) the best prediction results were obtained using PLSR and training using a combination of the SIs and HCSM as inputs (R2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and HCSM allows the most accurate yield estimation. The results of this study indicate that the crop plant height extracted from UAV-based hyperspectral measurements can improve yield estimation, and that the comparative analysis of PLSR, ANN, and RF regression techniques can provide a reference for agricultural management.

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Section: Yield Estimation Using Partial Least Squares Regression Ranmentioning

confidence: 99%

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

Tao

Feng

et al. 2020

Sensors

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“…The images can be acquired using a still camera [4,10,96] in a laboratory or under field conditions, and also by other optical or multispectral proximal sensors, on-the-go using ATVs [12,40,97], other terrestrial autonomous vehicles [7,48] including autonomous robot systems [15,102,106], UAVs [101,105] that cope with the limitations of ground vehicles regarding field conditions (slopes and soil) or in a more simple way on foot with a smartphone [57].…”

Section: A-data-driven Models Based On Computer Vision and Image Processing (N = 50)mentioning

confidence: 99%

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

2021

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Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.

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“…They are equipped with cameras and lamps, allowing them to acquire highquality images in the field on a large scale. They enable several actions in the field such as the detection of diseases [10], automated trimming of grapevines in the winter [11], estimation of vigor, detection of fruit [12], fertilization of grapes [13], estimation of grape size [14], large-scale phenotyping [15,16], automatic bagging of grapes [17], automatic detection of crates in vineyards [18], and multi-spectral 3D reconstruction of vines [19]. A more-complete technological survey has already been done by Matese et al [20].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision and Deep Learning for Precision Viticulture

Mohimont

Alin

Rondeau³

et al. 2022

Agronomy

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During the last decades, researchers have developed novel computing methods to help viticulturists solve their problems, primarily those linked to yield estimation of their crops. This article aims to summarize the existing research associated with computer vision and viticulture. It focuses on approaches that use RGB images directly obtained from parcels, ranging from classic image analysis methods to Machine Learning, including novel Deep Learning techniques. We intend to produce a complete analysis accessible to everyone, including non-specialized readers, to discuss the recent progress of artificial intelligence (AI) in viticulture. To this purpose, we present work focusing on detecting grapevine flowers, grapes, and berries in the first sections of this article. In the last sections, we present different methods for yield estimation and the problems that arise with this task.

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In-Field Grape Cluster Size Assessment for Vine Yield Estimation Using a Mobile Robot and a Consumer Level RGB-D Camera

Cited by 50 publications

References 25 publications

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

Computer Vision and Deep Learning for Precision Viticulture

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