Related Experiment Video
Updated: Jun 21, 2025
06:41
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
Published on: March 28, 2025
761
An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart
Muhammad Farrukh Shahid1, Tariq J S Khanzada2,3, Muhammad Ahtisham Aslam4
1FAST School of Computing, National University of Computer & Emerging Sciences, Karachi, 75030, Pakistan. mfarrukh.shahid@nu.edu.pk.
Plant Methods
|July 14, 2024
Summary
Early detection of cotton plant diseases using deep learning (DL) and computer vision (CV) significantly improves crop yield and economic outcomes. Continuous Wavelet Transform (CWT) features enhanced DL model performance for disease identification.
Area of Science:
- Agricultural Science
- Computer Science
- Artificial Intelligence
Background:
- Agriculture is vital for economic stability, but crop diseases and pests threaten yields.
- Early detection and prevention are critical for successful crop management and economic prosperity.
Purpose of the Study:
- To develop a framework for detecting healthy and unhealthy cotton plants using advanced computer vision and artificial intelligence.
- To enhance the accuracy of disease detection through feature extraction and ensemble learning.
Main Methods:
- Utilized deep learning (DL) models including AlexNet, GoogLeNet, InceptionV3, and VGG-19.
- Employed feature extraction techniques: Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT).
- Implemented an ensemble learning framework to fuse individual model predictions for improved accuracy.
Main Results:
- Features extracted using CWT outperformed those from FFT for DL model input.
- GoogLeNet achieved 93.4% accuracy with CWT features, closely followed by AlexNet (93.4%) and InceptionV3 (91.8%).
- The ensemble learning framework reached 98.4% accuracy using CWT features, outperforming FFT features.
Conclusions:
- Scalogram features extracted via CWT enable more accurate plant condition detection using DL models.
- Early disease detection in cotton plants leads to improved yields and profitability, positively impacting the economy.
