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Deep convolutional models for robust multi-crop disease recognition in real-world conditions.

Anubhava Srivastava¹, Amit Bhola², Jaroslav Frnda^3,4,5

¹Department of Computer Science and Engineering, Faculty of Engineering Sciences & Technology, Adani University, Ahmedabad, India.

Scientific Reports

|May 12, 2026

Summary

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A new deep learning system identifies crop diseases from leaf images using MobileNetV3. This web-based tool provides disease names and treatments, aiding farmers globally.

Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Crop diseases cause significant agricultural losses globally.
Limited availability of diagnostic experts exacerbates the problem in many regions.
Deep learning offers potential for automated plant disease identification from images.

Purpose of the Study:

To develop and deploy a web-based deep learning system for detecting diseases in multiple crops.
To compare the performance of different deep learning architectures for crop disease classification.
To create a user-friendly, multilingual platform for farmers to access disease diagnosis and treatment information.

Main Methods:

Compared MobileNetV3, EfficientNetB4, and ResNet50 using transfer learning on public datasets.

Keywords:

Convolutional neural networks (CNNs)Crop disease detection Deep learning EfficientNetB4 Fruits and vegetables classification MobileNetV3 Progressive web application Real-time disease diagnosis ResNet50 Sustainable agriculture

Related Experiment Videos

Integrated MobileNetV3 as the primary model due to its balance of accuracy, speed, and deployability.

Incorporated a super-resolution preprocessing step (Real-ESRGAN) to handle varying image quality.

Utilized Grad-CAM for visualizing disease-affected areas, enhancing model interpretability.

Developed a multilingual Progressive Web Application (PWA) for user image submission and AI-driven results.

Employed a Large Language Model (LLM) to generate treatment options based on structured disease metadata.

Main Results:

MobileNetV3 demonstrated superior performance considering classification accuracy, prediction time, and deployment feasibility.
The super-resolution step improved handling of diverse image qualities.
Grad-CAM effectively highlighted disease-specific regions in leaf images.
The PWA successfully integrated the deep learning model and LLM for disease prediction and treatment recommendations.
The system achieved good performance on test sets, despite acknowledged dataset biases.

Conclusions:

The developed system serves as a practical digital agriculture decision-support tool.
The system demonstrates the feasibility of deploying deep learning for crop disease detection via a PWA.
Further field-level validation and model improvements are necessary to address real-world complexities and dataset limitations.