Enhancing agricultural health with AI: Drone-based machine learning for mango tree disease detection
Year: 2024
Type: article
Abstract: In the agriculture sector, timely detection of diseases in fruit trees is a significant challenge, leading to substantial economic losses. Automated detection of diseases in fruit trees, particularly mango trees, is crucial to minimize these losses by enabling early intervention. This research explores the use of drone-captured multispectral images combined with deep learning and computer vision techniques to detect diseases in mango trees. The proposed system leverages various pre-trained Convolutional Neural Network (CNN) models, such as YOLOv5, Detectron2, and Faster R-CNN, to achieve optimal accuracy. Data augmentation techniques are employed to address data skewness and overfitting, while Generative Adversarial Networks (GANs) enhance image quality. The system aims to provide a scalable solution for early disease detection, thereby reducing economic losses and supporting the agricultural sector's growth.
Authors: Ali Husnain, Aftab Ahmad, Ayesha Saeed
Institutions: University of Chicago, University of Lahore
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4401708137