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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model.

Malathi Chilakalapudi¹, Sheela Jayachandran¹

¹SCOPE, VIT-AP University, Amaravathi, Andhra Pradesh, India.

Peerj. Computer Science

|June 26, 2025

Summary

This study introduces an advanced framework for precise crop disease detection and classification, significantly improving accuracy and reducing response times for better agricultural management.

Keywords:

Adaptive anisotropic diffusion Crop disease diagnosis Fuzzy set Moving gorilla remora algorithm

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Current crop disease diagnosis methods lack precision, accuracy, and speed, hindering effective management.
Existing techniques face limitations in classification accuracy and timely detection, impacting crop yields.

Purpose of the Study:

To develop an improved framework for crop disease detection and classification using multifaceted analysis.
To enhance the precision, accuracy, and efficiency of agricultural disease diagnosis.

Main Methods:

Implemented adaptive anisotropic diffusion for agro-image denoising to ensure data quality.
Utilized a Fuzzy U-Net++ model for enhanced image segmentation with fuzzy decision-making.
Introduced the Moving Gorilla Remora Algorithm (MGRA) with convolutional operations for optimal feature selection.
Employed a LeNet-inspired architecture for disease classification.

Main Results:

Achieved an 8.5% improvement in disease classification precision and 8.3% higher accuracy.
Demonstrated a 9.4% improvement in recall and a 4.5% reduction in time delay.
Increased the area under the curve (AUC) by 5.9% and specificity by 6.5%.

Conclusions:

The proposed framework significantly enhances crop disease detection and classification compared to existing methods.
This advancement promises more effective and efficient crop management through precision, accuracy, and timeliness.
The research paves the way for preemptive measures in agricultural health, boosting crop resilience and yield.