Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization

Modernizing and mechanizing agriculture are vital to increasing productivity and meeting the growing global food demand. Timely harvesting decisions, traditionally based on farmers’ experience, are crucial for crop management. This study introduces the Brinjal Harvesting Decision System (BHDS), an a...

Full description

Saved in:
Bibliographic Details
Main Authors: T. Tamilarasi, P. Muthulakshmi, Seyed-Hassan Miraei Ashtiani
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:AgriEngineering
Subjects:
Online Access:https://www.mdpi.com/2624-7402/7/6/196
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1839655129295355904
author T. Tamilarasi
P. Muthulakshmi
Seyed-Hassan Miraei Ashtiani
author_facet T. Tamilarasi
P. Muthulakshmi
Seyed-Hassan Miraei Ashtiani
author_sort T. Tamilarasi
collection DOAJ
description Modernizing and mechanizing agriculture are vital to increasing productivity and meeting the growing global food demand. Timely harvesting decisions, traditionally based on farmers’ experience, are crucial for crop management. This study introduces the Brinjal Harvesting Decision System (BHDS), an automated, real-time framework designed to optimize harvesting decisions using a portable, low-power edge computing device. Unlike conventional object detection models, which require substantial pre-training and curated datasets, the BHDS integrates automated data acquisition and dynamic image quality assessment, enabling effective operation with minimal data input. Tested on diverse farm layouts, the BHDS achieved 95.53% accuracy in data collection and captured quality images within an average of 3 s, reducing both time and energy for dataset creation. The brinjal detection algorithm employs pixel-based methods, including background elimination, K-means clustering, and symmetry testing for precise identification. Implemented on a portable edge device and tested in actual farmland, the system demonstrated 79% segmentation accuracy, 87.48% detection precision, and an F1-score of 87.53%, with an average detection time of 3.5 s. The prediction algorithm identifies ready-to-harvest brinjals with 89.80% accuracy in just 0.029 s. Moreover, the system’s low energy consumption, operating for over 7 h on a 10,000 mAh power bank, demonstrates its practicality for agricultural edge applications. The BHDS provides an efficient, cost-effective solution for automating harvesting decisions, minimizing manual data processing, reducing computational overhead, and maintaining high precision and operational efficiency.
format Article
id doaj-art-40a4b28dfdfb49ff822fb82cb6bd1fa2
institution Matheson Library
issn 2624-7402
language English
publishDate 2025-06-01
publisher MDPI AG
record_format Article
series AgriEngineering
spelling doaj-art-40a4b28dfdfb49ff822fb82cb6bd1fa22025-06-25T13:20:00ZengMDPI AGAgriEngineering2624-74022025-06-017619610.3390/agriengineering7060196Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision OptimizationT. Tamilarasi0P. Muthulakshmi1Seyed-Hassan Miraei Ashtiani2Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur 603203, IndiaDepartment of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur 603203, IndiaFaculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, CanadaModernizing and mechanizing agriculture are vital to increasing productivity and meeting the growing global food demand. Timely harvesting decisions, traditionally based on farmers’ experience, are crucial for crop management. This study introduces the Brinjal Harvesting Decision System (BHDS), an automated, real-time framework designed to optimize harvesting decisions using a portable, low-power edge computing device. Unlike conventional object detection models, which require substantial pre-training and curated datasets, the BHDS integrates automated data acquisition and dynamic image quality assessment, enabling effective operation with minimal data input. Tested on diverse farm layouts, the BHDS achieved 95.53% accuracy in data collection and captured quality images within an average of 3 s, reducing both time and energy for dataset creation. The brinjal detection algorithm employs pixel-based methods, including background elimination, K-means clustering, and symmetry testing for precise identification. Implemented on a portable edge device and tested in actual farmland, the system demonstrated 79% segmentation accuracy, 87.48% detection precision, and an F1-score of 87.53%, with an average detection time of 3.5 s. The prediction algorithm identifies ready-to-harvest brinjals with 89.80% accuracy in just 0.029 s. Moreover, the system’s low energy consumption, operating for over 7 h on a 10,000 mAh power bank, demonstrates its practicality for agricultural edge applications. The BHDS provides an efficient, cost-effective solution for automating harvesting decisions, minimizing manual data processing, reducing computational overhead, and maintaining high precision and operational efficiency.https://www.mdpi.com/2624-7402/7/6/196brinjalmaturity predictiondetection accuracyIoUsmart agriculturereal-time object detection
spellingShingle T. Tamilarasi
P. Muthulakshmi
Seyed-Hassan Miraei Ashtiani
Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization
AgriEngineering
brinjal
maturity prediction
detection accuracy
IoU
smart agriculture
real-time object detection
title Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization
title_full Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization
title_fullStr Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization
title_full_unstemmed Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization
title_short Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization
title_sort smart edge computing framework for real time brinjal harvest decision optimization
topic brinjal
maturity prediction
detection accuracy
IoU
smart agriculture
real-time object detection
url https://www.mdpi.com/2624-7402/7/6/196
work_keys_str_mv AT ttamilarasi smartedgecomputingframeworkforrealtimebrinjalharvestdecisionoptimization
AT pmuthulakshmi smartedgecomputingframeworkforrealtimebrinjalharvestdecisionoptimization
AT seyedhassanmiraeiashtiani smartedgecomputingframeworkforrealtimebrinjalharvestdecisionoptimization