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

Luis David Viveros Escamilla, Alfonso Gómez-Espinosa (), Jesús Arturo Escobedo Cabello and Jose Antonio Cantoral-Ceballos
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Luis David Viveros Escamilla: Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Av. Epigmenio González 500, Fracc, San Pablo 76130, Mexico
Alfonso Gómez-Espinosa: Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Av. Epigmenio González 500, Fracc, San Pablo 76130, Mexico
Jesús Arturo Escobedo Cabello: Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Av. Epigmenio González 500, Fracc, San Pablo 76130, Mexico
Jose Antonio Cantoral-Ceballos: Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Av. Epigmenio González 500, Fracc, San Pablo 76130, Mexico

Agriculture, 2024, vol. 14, issue 3, 1-31

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.

Keywords: yield estimation; maturity detection; deep learning; computer vision; precision agriculture; sweet peppers (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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