LEAF: a process-based model of berry ripening in vineyards

Cartenì, Fabrizio; Rossi, Claudio; Marcos, R. de la Fuente; Porras, Ignasi; Basile, Boris; Scognamiglio, Pasquale; Teobaldelli, Maurizio; Mataffo, Alessandro; Mazzoleni, Stefano; García-Tejera, Omar; Girona, J.; Queiroz, Jorge; Cabral, Inês L.

doi:10.1109/metroagrifor.2019.8909224

Cited by 1 publication

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“…Crop growth models are based on "first principles", including the aforementioned data, to simulate crop development. A number of crop growth models have been proposed in the literature to simulate grapevine growth: IVINE to evaluate environmental forcing effects [45,46], VICMOTO numerical model to study the influence of meteorology and climate [47,48], the generic time-step crop model STICS [49], LEAF process-based model [50] to predict the vegetative performance of the vineyards, etc. Input data to crop growth models are usually compiled over an extended time period of many years.…”

Section: Crop Growth Modelsmentioning

confidence: 99%

Machine Vision for Ripeness Estimation in Viticulture Automation

et al. 2021

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Ripeness estimation of fruits and vegetables is a key factor for the optimization of field management and the harvesting of the desired product quality. Typical ripeness estimation involves multiple manual samplings before harvest followed by chemical analyses. Machine vision has paved the way for agricultural automation by introducing quicker, cost-effective, and non-destructive methods. This work comprehensively surveys the most recent applications of machine vision techniques for ripeness estimation. Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes. The aim of this work is to provide an overview of the state-of-the-art algorithms by covering a wide range of applications. The potential of current machine vision techniques for specific viticulture applications is also analyzed. Problems, limitations of each technique, and future trends are discussed. Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined.

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