Plant Disease Detection Using Clustering Based Segmentation and Neural Networks

Mohan, Aditya; Srivastava, Kushagra; Malhotra, Garima; Khan, Nafis Uddin

doi:10.1109/pdgc50313.2020.9315856

Cited by 2 publications

(1 citation statement)

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“…The categorization of classifiers is based on various rules such as parametric and nonparametric, object-based, and pixel-based. Supervised and unsupervised methods are the two broad assortments of these classifiers [124].…”

Section: Image Classificationmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

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Section: Image Classificationmentioning

confidence: 99%