FIG.1 — Proposed hybrid algorithm
Automatic Radish Wilt Detection Using Image Processing Based Techniques and Machine Learning Algorithm
This research paper presents a hybrid methodology for detecting fusarium wilt in radish crops by combining image processing and machine learning techniques. The approach involves segmenting crop images into healthy vegetation, ground, and packing material using an HSV decision tree algorithm, followed by reconstructing the image and isolating wilt traces through comparison and noise elimination with k-means clustering. The final wilt-affected regions are mapped onto the original image using contouring.
The proposed method demonstrates improved accuracy over traditional approaches that use image processing or machine learning independently, offering a robust solution for automated plant disease detection in precision agriculture.
Cited by: this article received its first citation in the Springer Nature chapter “Artificial Intelligence in Horticultural Crop Improvement,” marking the achievement of h-index 1 for this publication.
- UAV imagery
- HSV decision tree
- k-means clustering
- Precision agriculture