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Deep learning based apple leaf disease detection using spatially modulated continuouslayer.

Aniket K Shahade¹, Priyanka V Deshmukh²

¹Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India. aniket.shahade@sitpune.edu.in.

Scientific Reports

|June 13, 2026

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A new deep learning model with a ContinuousLayer accurately detects apple leaf diseases like black rot, rust, and scab. This automated system improves precision agriculture by overcoming manual diagnosis limitations.

Area of Science:

Plant Pathology
Computer Vision
Machine Learning

Background:

Accurate apple leaf disease identification is crucial for sustainable agriculture.
Manual diagnosis is labor-intensive and prone to errors, impacting crop yields.
Existing deep learning models face challenges with spatial feature extraction and dataset imbalance.

Purpose of the Study:

To develop a novel deep learning framework for automated apple leaf disease classification.
To introduce and evaluate a custom ContinuousLayer for enhanced spatial feature extraction.
To address dataset imbalance and improve classification accuracy for diseases including black rot, rust, and scab.

Main Methods:

A deep learning framework incorporating a custom ContinuousLayer was designed.

Keywords:

Apple Leaf Disease ContinuousLayer Convolutional Neural Network Gaussian Processes.Precision Agriculture Spatial Feature Learning

Related Experiment Videos

The ContinuousLayer utilizes trainable Gaussian basis functions for spatial feature modulation.

Dataset imbalance was managed using strategic resampling (bicubic up-sampling); a hybrid composite loss function was employed.

Main Results:

The model achieved 98.63% test accuracy on a balanced dataset of 3,164 images.
F1-scores ranged from 0.98 to 1.00 across all disease classes (black rot, rust, scab, healthy).
The ContinuousLayer demonstrated superior performance in capturing disease-specific spatial patterns compared to baseline models.

Conclusions:

The novel deep learning framework with ContinuousLayer offers a highly accurate tool for automated apple leaf disease detection.
This approach shows significant potential for enhancing precision agriculture practices in controlled environments.
Integrating mathematically inspired layers into CNNs can advance plant pathology diagnostics.