In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment.

Abstract

An early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on manual counting of fruits or flowers by workers is a time consuming and expensive process and it is not feasible for large fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. In a typical image classification process, the task is not only to specify the presence or absence of a given object on a specific location, while counting how many objects are present in the scene. The success of these tasks largely depends on the availability of a large amount of training samples. This paper presents a detector of bunches of one fruit, grape, based on a deep convolutional neural network trained to detect vine bunches directly on the field. Experimental results show a 91% mean Average Precision.

Plant phenotyping relevance

ブドウ房を圃場画像から自動検出・計数し、収量推定に用いる画像ベースの植物器官計測法が中心である。

Code and data availability

The paper's key public asset, the GrapeCS-ML image dataset (doi:10.26189/5da7a8603c55c), is openly available but its URL is not among the allowed_urls, so it cannot be listed. The Mask R-CNN GitHub repository is a generic third-party library, not the authors' analysis code. The internal dataset is explicitly internal-­

No evidence-backed public reproduction asset is currently recorded.