Automated System for Comprehensive Plant Disease Analysis

Olfa Mzoughi

doi:10.1007/978-981-99-8684-2_4

Automated System for Comprehensive Plant Disease Analysis

Olfa Mzoughi

Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

This chapter highlights the importance of automated plant disease analysis for agricultural enhancement. It addresses two critical challenges: disease identification and severity assessment. The chapter delineates the transformative impact of deep learning techniques, including transfer learning, object detection, and segmentation. Moreover, it recommends the adoption of weakly supervised learning to overcome inherent classification complexities as well as annotation challenges. This study offers comprehensive practical insights to advance plant disease analysis.

Original language	English
Title of host publication	Applications of Computer Vision and Drone Technology in Agriculture 4.0
Publisher	Springer Nature
Pages	49-64
Number of pages	16
ISBN (Electronic)	9789819986842
DOIs	https://doi.org/10.1007/978-981-99-8684-2_4
State	Published - 1 Jan 2024

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10.1007/978-981-99-8684-2_4

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AB - This chapter highlights the importance of automated plant disease analysis for agricultural enhancement. It addresses two critical challenges: disease identification and severity assessment. The chapter delineates the transformative impact of deep learning techniques, including transfer learning, object detection, and segmentation. Moreover, it recommends the adoption of weakly supervised learning to overcome inherent classification complexities as well as annotation challenges. This study offers comprehensive practical insights to advance plant disease analysis.

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Automated System for Comprehensive Plant Disease Analysis

Abstract

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