Segmentation Algorithms For Accurate Decision Of Banana Leaf Diseases In Precision Agriculture
Year: 2024
Type: article
Abstract: Banana cultivation plays a crucial role in the agricultural economy, but its productivity is significantly affected by various leaf diseases. Early and accurate identification of banana leaf diseases is essential for effective disease management and yield optimization. This study presents an automated approach for detecting and classifying banana leaf diseases using a segmentation algorithm. The proposed method involves image preprocessing, segmentation, and feature extraction techniques to isolate diseased regions from healthy leaf areas. By applying advanced image processing and machine learning techniques, the system efficiently identifies common banana leaf diseases such as Sigatoka, Cordana, and bacterial infections. The results demonstrate the effectiveness of the segmentation algorithm in accurately distinguishing diseased areas, thereby assisting farmers and researchers in early disease detection. This approach enhances precision agriculture by providing a reliable and automated solution for monitoring plant health, ultimately contributing to improved crop management and reduced economic losses.
Source: Nanotechnology Perceptions
Author: Vishnu Prabhakar. V, Dr. N. Sudha
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4410215245