Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach

Arab, Sara Tokhi; Noguchi, Ryozo; Matsushita, Shusuke; Ahamed, Tofael

doi:10.1016/j.rsase.2021.100485

Cited by 37 publications

(29 citation statements)

References 30 publications

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“…Finally, adding new indicators could be done by crossing different sources. For example, satellite images have recently been used to predict vine yields [165]. New models will also be needed to consider the different nature of the data (RGB images, satellite images, temporal series, etc.).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Computer Vision and Deep Learning for Precision Viticulture

Mohimont

Alin

Rondeau³

et al. 2022

Agronomy

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“…vigor, yield, etc.) through remotely sensed VIs and statistical or deep learning methods (Arab et al, 2021;Ballesteros et al, 2020;Di Gennaro et al, 2019;Matese & Di Gennaro, 2021;Sun et al, 2017) cannot provide information on the causes that may lead to a reduction in harvested yield. By contrast, the dynamic of growth processes is fully considered in process-based models where crop physiological responses are modeled in relation to the variability of climate, soil and management conditions and environmental stresses (e.g.…”

Section: Discussionmentioning

confidence: 99%

Use of remote sensing-derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub-field level

Leolini

Bregaglio²,

Ginaldi³

et al. 2022

Precision Agric

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Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2 = 0.61; RMSE = 19.86 dry matter g m−2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2 = 0.47; RMSE = 75.52 dry matter g m−2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level.

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“…Khan et al [57] used three machine learning methods (LM, SCG, and BR) and predict future fruit production from 1980 to 2025 with an accuracy of 76.30 ± 2%. Arab et al [58] used an artificial neural network and regression analysis to find different vegetation index (NDVI) of grape fruit with accuracy 2018 (R = 0.95).…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

et al. 2021

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Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

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Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach

Cited by 37 publications

References 30 publications

Computer Vision and Deep Learning for Precision Viticulture

Computer Vision and Deep Learning for Precision Viticulture

Use of remote sensing-derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub-field level

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

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