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Home
Enhanced Corn Leaf Disease Detection Using Sharpness-aware Minimization Optimized Cnns.

Home
Enhanced Corn Leaf Disease Detection Using Sharpness-aware Minimization Optimized Cnns.

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A Mouse Model for Laser-induced Choroidal Neovascularization

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Published on: December 27, 2015

Enhanced corn leaf disease detection using sharpness-aware minimization optimized CNNs.

Manoj Kumar Sharma¹, Richa Sharma², Gireesh Kumar³

¹Manipal University Jaipur, Jaipur, Rajasthan, 303007, India.

|April 1, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Sharpness-Aware Minimization (SAM) optimizes Convolutional Neural Networks (CNNs) for crop disease detection. This approach improves model generalization, achieving 99.66% accuracy in identifying corn leaf diseases, crucial for food security.

Keywords:

Agricultural AI CNNs Corn leaf disease detection Plant disease classification SAM

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Specific and Accurate Detection of the Citrus Greening Pathogen Candidatus liberibacter spp. Using Conventional PCR on Citrus Leaf Tissue Samples

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Published on: June 29, 2018

Area of Science:

Agricultural Science
Computer Science
Machine Learning

Background:

Crop diseases pose a significant threat to global food security, impacting yield and quality.
Traditional disease diagnostics are labor-intensive and prone to human error.
Current deep learning models struggle with generalization due to sharp loss landscapes.

Purpose of the Study:

To optimize Convolutional Neural Network (CNN) models for crop disease diagnosis using Sharpness-Aware Minimization (SAM).
To address the generalization limitations of existing deep learning solutions in agriculture.
To enhance the accuracy and efficiency of automated crop disease detection.

Main Methods:

Implemented Sharpness-Aware Minimization (SAM) to optimize CNNs, minimizing both training loss and loss landscape sharpness.

Trained and evaluated the optimized CNN on 60,000 augmented corn leaf images across four disease classes.

Compared SAM's performance against conventional optimizers like Adam and Stochastic Gradient Descent (SGD).

Main Results:

Achieved 99.66% test accuracy with a 0.33% classification error rate, surpassing Adam (98.44%) and SGD.
Reported state-of-the-art metrics: 99% average precision, 99.66% F1-score, and 0.0013% Mean Squared Error (MSE).
Quantized model achieved fast inference (22.7 ms/image on Raspberry Pi 4), reduced overfitting, and improved feature discriminability.

Conclusions:

SAM-based CNN optimization significantly enhances generalization and accuracy in crop disease detection.
The optimized model demonstrates practical viability for precision agriculture in resource-constrained environments.
This work facilitates scalable automation of disease management, bridging deep learning advancements with agricultural needs.