Mouneesh et al. (2026) Automated Fruit Disease Detection and Smart Irrigation System Using YOLOv8

Identification

• Journal: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
• Year: 2026
• Date: 2026-04-03
• Authors: G . Mouneesh, Dr. V. Phani Bhushan, T. Bhanu Prakash, T. P. Durga Charan, T. Sri Hari, S. Venkateswarlu
• DOI: 10.55041/ijsrem58961

Research Groups

Department of Electronics and Communication Engineering, PBR Visvodaya Institute of Technology & Science, Kavali (Autonomous), SPSR Nellore (Dt.), Andhra Pradesh, India

Short Summary

This paper presents an automated system for precision agriculture that integrates YOLOv8-based fruit disease detection and growth monitoring with a smart irrigation system, utilizing real-time sensor data for soil moisture and pH to optimize water usage and crop management.

Objective

• To develop and implement an automated system for precision agriculture that combines real-time fruit disease detection and growth monitoring using deep learning with a smart irrigation system based on soil condition analysis.

Study Configuration

• Spatial Scale: Individual fruit trees/plants (monitoring fruits on trees, local soil conditions).
• Temporal Scale: Continuous monitoring in real-time.

Methodology and Data

• Models used: YOLOv8 deep learning model.
• Data sources:
  • USB camera (for continuous fruit monitoring, disease detection, growth, and maturity tracking).
  • Soil moisture sensor (for evaluating soil dryness).
  • pH sensor (for measuring soil acidity or alkalinity).
  • GSM module (for message alerts).
  • DC water pump (controlled based on sensor readings).

Main Results

• The system successfully integrates image-based fruit monitoring and soil condition analysis for comprehensive crop management.
• It enables automated control of irrigation by switching a DC water pump ON/OFF based on predefined soil moisture thresholds.
• The combined approach facilitates intelligent decision-making in smart farming.
• The system is designed to achieve better crop management, improved yield, and efficient water usage.

Contributions

• Development of a comprehensive, integrated system for precision agriculture combining deep learning for fruit monitoring with sensor-based smart irrigation.
• Real-time detection and tracking of fruit growth, maturity, and potential diseases using the YOLOv8 model.
• Automated and efficient water management based on continuous soil moisture and pH sensing.
• Integration of multiple technologies (deep learning, environmental sensors, automated irrigation, and GSM alerts) into a practical smart farming solution.

Funding

[No specific funding information was provided in the paper text.]

Citation

@article{Mouneesh2026Automated,
  author = {Mouneesh, G . and Bhushan, Dr. V. Phani and Prakash, T. Bhanu and Charan, T. P. Durga and Hari, T. Sri and Venkateswarlu, S.},
  title = {Automated Fruit Disease Detection and Smart Irrigation System Using YOLOv8},
  journal = {INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT},
  year = {2026},
  doi = {10.55041/ijsrem58961},
  url = {https://doi.org/10.55041/ijsrem58961}
}