1 Department of Computer Science, University of Chicago, USA.
2 Department of Computer Science, American National University, USA.
3 Department of Computer Science, University of Lahore, Lahore, Pakistan.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1267-1276
Received on 02 July 2024; revised on 13 August 2024; accepted on 15 August 2024
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.
YOLOv5; Drone; Convolutional Neural Network (CNN); Generative Adversarial Networks (GANs); WebODM
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Ali Husnain, Aftab Ahmad and Ayesha Saeed. Enhancing agricultural health with AI: Drone-based machine learning for mango tree disease detection. World Journal of Advanced Research and Reviews, 2024, 23(2), 1267-1276. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2455