Dhivya, S Bio-Inspired Deep Learning Framework for Accurate Strawberry Leaf Disease Classification. Bio-Inspired Deep Learning Framework for Accurate Strawberry Leaf Disease Classification.

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Abstract

Strawberry plants, like many other crops, are vulnerable to a
range of leaf diseases, which can cause substantial yield reductions. For
crop loss to be avoided and high productivity levels to be maintained,
timely diagnosis and precise classification of these diseases are essential.
Conventional manual techniques for identifying diseases are frequently
slow, error-prone, and limited in precision, particularly when recognizing
early or subtle symptoms. To overcome these limitations, this study
introduces an automated deep learning framework designed for precise
classification of strawberry leaf diseases, employing advanced
Convolutional and Recurrent Neural Networks (CNN and RNN) optimized
with bacterial foraging and particle swarm optimization techniques. The
framework combines CNN and RNN architectures, optimized to deliver
high accuracy in distinguishing fungal and viral infections. By employing
K-means clustering, Sparse Principal Component Analysis (PCA), and a
CNN with ReLU activation, the model effectively extracts and refines
features linked to disease. Further, region-growing segmentation and an
optimized RNN enhance detection accuracy by capturing essential spatial
and temporal patterns. Experimental tests using the PlantVillage dataset
indicate a classification accuracy of 99%, highlighting the model's potential
for early disease control and real-time agricultural surveillance. This
strategy has a lot of potential to improve disease control methods, which
will eventually increase crop productivity and yield.

Item Type: Article
Uncontrolled Keywords: Strawberry Plant Leaves, Convolutional Neural Networks(CNN), Recurrent Neural Networks(RNN), and Sparse Principal Component Analysis (PCA).
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Dr. B Sivakumar
Date Deposited: 18 Apr 2026 06:12
Last Modified: 18 Apr 2026 06:12
URI: https://ir.psgcas.ac.in/id/eprint/2807

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