Automated detection of grapes and leaves in viticulture with a YOLOv2 neural network

Andreza Aparecida Dos Santos,Sandra Eliza Fontes De Avila,Thiago Teixeira Dos Santos

doi:10.20396/revpibic262018155

Abstract

In this work, we modeled the problem of detection of fruit and leaves in viticulture for proximal applications as a supervised machine learning task. We created and manually labeled a database of images obtained at Guaspari Winery. In total, the database consists of 11.883 images of bunch of grapes and leaves. We trained a convolutional network with YOLOv2 architecture to locate and classify bunch of grapes and leaves. Quantitative tests have shown results for detection and classification with precision of 100%, recall of 74,22% and F1-Score up to 85,2% for the class “grape”. Also, qualitative tests show that the model generalizes well when tested on photographs of other grape varieties. These results are promising and are moving towards the possibility of application in the field.

Highlights

The detection and classification of fruits are essential components in automation applications in the field of precision agriculture
We built a database from images obtained from Guaspari Winery
The samples are composed of bounding boxes containing grape clusters and leaves

Summary

Introduction

Introduction The detection and classification of fruits are essential components in automation applications in the field of precision agriculture. Proximal sensing becomes more appropriate where image-based techniques and neural networks constitute the current state of the art [1]. As the goal is to be able to predict through images from a camera that is embedded in a mobile field system, we chose the YOLOv2 neural network [2] for the task because of its real-time performance on prediction. We built a database from images obtained from Guaspari Winery.

Results

Conclusion