In-field grape berries counting for yield estimation using dilated CNNs

TL;DR

This paper presents a smartphone-based tool utilizing dilated CNNs, adapted from crowd counting algorithms, to accurately estimate grape berry yields, advancing cost-effective precision agriculture practices.

Contribution

It introduces a novel application of dilated CNNs for in-field grape berry counting, enabling scalable and accurate yield estimation using smartphones.

Findings

High accuracy in grape berry counting demonstrated

Effective adaptation of crowd counting CNNs to agriculture

Potential for cost-effective yield estimation

Abstract

Digital technologies ignited a revolution in the agrifood domain known as precision agriculture: a main question for enabling precision agriculture at scale is if accurate product quality control can be made available at minimal cost, leveraging existing technologies and agronomists' skills. As a contribution along this direction we demonstrate a tool for accurate fruit yield estimation from smartphone cameras, by adapting Deep Learning algorithms originally developed for crowd counting.