Maturity Recognition and Fruit Counting for Sweet Peppers in Greenhouses Using Deep Learning Neural Networks

Abstract

This study presents an approach to address the challenges of recognizing the maturity stage and counting sweet peppers of varying colors (green, yellow, orange, and red) within greenhouse environments. The methodology leverages the YOLOv5 model for real-time object detection, classification, and localization, coupled with the DeepSORT algorithm for efficient tracking. The system was successfully implemented to monitor sweet pepper production, and some challenges related to this environment, namely occlusions and the presence of leaves and branches, were effectively overcome. We evaluated our algorithm using real-world data collected in a sweet pepper greenhouse. A dataset comprising 1863 images was meticulously compiled to enhance the study, incorporating diverse sweet pepper varieties and maturity levels. Additionally, the study emphasized the role of confidence levels in object recognition, achieving a confidence level of 0.973. Furthermore, the DeepSORT algorithm was successfully applied for counting sweet peppers, demonstrating an accuracy level of 85.7% in two simulated environments under challenging conditions, such as varied lighting and inaccuracies in maturity level assessment.

Plant phenotyping relevance

深層学習による果実の成熟段階認識と計数が研究の中心であり、植物器官の状態（成熟度）を画像から抽出・評価しているため、植物フェノタイピング手法として含める。

Code and data availability

The authors explicitly state their dataset and supporting data are publicly available via their own GitHub repository (YOLOv5 + DeepSORT sweet pepper detection/counting), with an exact URL given in the text and Data Availability Statement. The Kaggle and Roboflow datasets are cited external/prior datasets, not paper-.