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High-Performance Deep Learning for Instant Pest and Disease Detection in Precision Agriculture.

Muhammad Bilal¹, Asghar Ali Shah², Sagheer Abbas³

¹Riphah School of Computing & Innovation, Faculty of Computing Riphah International University Lahore Pakistan.

Food Science & Nutrition

|September 18, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning model for real-time pest and disease detection in crops, improving farm productivity and food security. The AI model is optimized for edge devices, enabling early detection in remote fields.

Keywords:

MobileNet and EfficientNet crop disease deep learning fusion model image processing pest detection transfer learning

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Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Global farm productivity faces significant threats from pests and diseases, leading to crop loss and food insecurity.
Current manual and traditional methods for pest and disease detection are labor-intensive, time-consuming, and prone to errors.
Traditional machine learning models lack field deployability for real-time agricultural applications.

Purpose of the Study:

To develop a high-performance deep learning fusion model for real-time detection of crop pests and diseases.
To optimize the model for deployment on edge devices for precision farming applications.
To provide a scalable, cost-effective, and accurate early detection framework to enhance global food security.

Main Methods:

A deep learning fusion model combining MobileNetV2 and EfficientNetB0 was developed.
The model was trained on the CCMT dataset, comprising over 127,000 images across 22 crop classes (cashew, cassava, maize, tomato).
Techniques including quantization, pruning, and knowledge distillation were used to optimize the model for edge deployment, achieving inference times under 10ms per image.

Main Results:

The fusion model achieved a global accuracy of 89.5%, with precision and recall of 95.68% and an F1-score of 95.67%.
The optimized model demonstrated superior performance compared to baseline CNN models like ResNet-50 and VGG-16.
The model successfully ran on low-power devices (smartphones, Raspberry Pi, drones) for real-time, cloud-independent detection, validated by field trials.

Conclusions:

The developed deep learning model offers a scalable, cost-effective, and accurate solution for early pest and disease detection in agriculture.
The model's edge deployability supports precision farming and enhances sustainable agriculture practices.
This framework contributes to global food security by enabling real-time monitoring and management of crop health in remote areas.