Work

Comparative Analysis of YOLOv8 and YOLOv9 Models for Real-Time Plant Disease Detection in Hydroponics

Year: 2024

Type: article

Abstract: Plant diseases are a significant threat to modern agricultural productivity. Hydroponic systems are also affected for various reasons. Reliable and efficient detection methods are essential for early intervention and management of diseases in hydroponics. This study investigates the use of You Only Look Once (YOLO) models, namely YOLOv8 and YOLOv9, for the detection of plant diseases in a hydroponic environment. A diverse dataset was prepared, comprising images from a hydroponics system setup and the New Plant Disease Image Dataset from Kaggle. Custom annotated images were used to train and test the models and compare their accuracy, processing speed, and robustness in hydroponic systems. The results showed that YOLOv9 is slightly better than YOLOv8 in terms of detection accuracy, as it achieved 88.38% compared to 87.22%, respectively. YOLOv8 requires less computational resources and takes relatively less time than YOLOv9 for real-time plant disease detection. Therefore, it is recommended for portable devices.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.8301

Authors: Abhishek Tripathi, Vinaya Gohokar, Rupali Kute

Institution: MIT World Peace University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $431