Non‐invasive setup for grape maturation classification using deep learning

Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association

(2020) Thiago T. Santos et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Development of predictive models for quality and maturation stage attributes of wine grapes using vis-nir reflectance spectroscopy

(2019) Daniel dos Santos Costa et al.   POSTHARVEST BIOLOGY AND TECHNOLOGY

Deep learning in agriculture: A survey

(2018) Andreas Kamilaris et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

In-field high throughput grapevine phenotyping with a consumer-grade depth camera

(2018) Annalisa Milella et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

(2017) Vivienne Sze et al.   PROCEEDINGS OF THE IEEE

Image analysis operations applied to hyperspectral images for non-invasive sensing of food quality – A comprehensive review

(2016) Gamal M. ElMasry et al.   BIOSYSTEMS ENGINEERING

Assessment of technological maturity parameters and anthocyanins in berries of cv. Sangiovese ( Vitis vinifera L.) by a portable vis/NIR device

(2016) Alejandra Ribera-Fonseca et al.   SCIENTIA HORTICULTURAE

Grape maturity estimation based on seed images and neural networks

(2014) Alex Zuñiga et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: A preliminary approach

(2013) Julio Nogales-Bueno et al.   FOOD CHEMISTRY

Chlorophyll Fluorescence Imaging for the Noninvasive Assessment of Anthocyanins in Whole Grape (Vitis viniferaL.) Bunches†

(2008) Giovanni Agati et al.   PHOTOCHEMISTRY AND PHOTOBIOLOGY