Version 1
: Received: 16 October 2024 / Approved: 16 October 2024 / Online: 16 October 2024 (11:46:14 CEST)
Version 2
: Received: 23 October 2024 / Approved: 24 October 2024 / Online: 24 October 2024 (14:22:16 CEST)
How to cite:
Sabit, M. N. Neural Network Model for Detecting Leaf Diseases and Assessing Invasive Species: Computational Approach. Preprints2024, 2024101287. https://doi.org/10.20944/preprints202410.1287.v2
Sabit, M. N. Neural Network Model for Detecting Leaf Diseases and Assessing Invasive Species: Computational Approach. Preprints 2024, 2024101287. https://doi.org/10.20944/preprints202410.1287.v2
Sabit, M. N. Neural Network Model for Detecting Leaf Diseases and Assessing Invasive Species: Computational Approach. Preprints2024, 2024101287. https://doi.org/10.20944/preprints202410.1287.v2
APA Style
Sabit, M. N. (2024). Neural Network Model for Detecting Leaf Diseases and Assessing Invasive Species: Computational Approach. Preprints. https://doi.org/10.20944/preprints202410.1287.v2
Chicago/Turabian Style
Sabit, M. N. 2024 "Neural Network Model for Detecting Leaf Diseases and Assessing Invasive Species: Computational Approach" Preprints. https://doi.org/10.20944/preprints202410.1287.v2
Abstract
Invasive species and plant diseases are critical threats to ecosystems and agriculture worldwide. Effective early detection of these threats can significantly mitigate their impact on biodiversity and crop yields. This paper presents a neural network-based model for detecting leaf diseases and identifying invasive species using advanced image processing techniques. The model integrates edge detection, color analysis, and morphological feature extraction to assess leaf health and species type. By automating the identification process, this approach offers an efficient and scalable solution for real-time ecological monitoring, contributing to conservation and agricultural sustainability efforts.
Keywords
Invasive Species; Leaf Morphology Neural Network Model
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.