What question did this study set out to answer?

This research aims to compare the effectiveness of hybrid LSTM-CNN and CNN methods in detecting grape crop diseases.

June 4, 2026MethodsX0 citationsOpen Access

Enhancing Grape Disease Detection: A Comparative Analysis of Hybrid CNN-LSTM and CNN Methods.

VMVinod MulikBharati Vidyapeeth Deemed University VPVinay PatilGovernment Ayurved College, Nanded

Key Points

This research aims to compare the effectiveness of hybrid LSTM-CNN and CNN methods in detecting grape crop diseases.
Methods trained and tested on balanced (4000 images) and imbalanced (4062 images) grape leaf datasets.
The imbalanced dataset consists of images from healthy, ESCA, leaf blight, and black rot classes.
Deep learning models designed for user-friendly deployment without technical expertise.
LSTM-CNN method achieved 100% accuracy on the balanced dataset, while CNN achieved 99.47%.
On the imbalanced dataset, LSTM-CNN achieved 100% accuracy and CNN achieved 97.89%.
LSTM-CNN outperformed CNN in both balanced and imbalanced datasets.

Abstract

Crop disease detection is crucial for maintaining high agricultural productivity and minimizing the financial losses of farmers. • This study compares the performance of a hybrid method based on integrating the Long Short-Term Memory algorithm (LSTM) and Convolutional Neural Network (CNN) with a standalone CNN method for grape crop disease detection and classification. • The proposed methods are trained and tested on a comprehensive balanced and imbalanced grape leaf dataset with 4000 and 4062 images, respectively. The imbalanced dataset has 423 images of the healthy class, 1383 of ESCA, 1076 of leaf blight, and 1180 images of the black rot class, while the balanced dataset has 1000 images in each class • The LSTM-CNN method achieved 100%, and the CNN method achieved 99.47 % accuracy on the balanced dataset. Also, in the case of the imbalanced dataset, 100% and 97.89%, respectively. Among these two methods implemented, the proposed LSTM-CNN method has achieved excellent results in terms of performance metrics. This study presents a deep learning-based system for automated leaf disease classification. The model is designed for end-user deployment, where users can upload a leaf image and receive an immediate prediction of the disease without requiring model retraining or technical expertise.

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