A Deep Learning–Based Method for Paddy Leaf Disease Detection and Growth Stage–Specific Treatment Recommendation

Summary

This study presents a deep learning–based method for predicting paddy leaf growth stages and classifying diseases, delivering stage-specific treatment recommendations through an automated, user-friendly decision support system for effective crop disease management.

Abstract

Paddy leaf diseases significantly affect rice yield and quality, making early detection and proper treatment essential for precision agriculture. This study proposes a deep learning–based decision support system for paddy leaf disease detection, growth-stage prediction, and stage-specific treatment recommendations. The dataset used in this study comprises paddy leaf images collected from multiple sources and categorized by growth stage and disease class. The dataset was divided into training (80%), validation (10%), and test (10%) sets to ensure proper model evaluation. For growth stage prediction, a lightweight Convolutional Neural Network (CNN) model was developed, while disease classification was performed using transfer learning models, including VGG16, ResNet50, InceptionV3, and MobileNetV2. An ensemble method based on average probability voting was used to improve classification performance. The models were evaluated using accuracy, precision, recall, and F1-score on an independent test set. The experimental results show that the ensemble model achieved higher accuracy compared to individual models, demonstrating improved robustness and generalization. The proposed system was implemented as a Streamlit web application that provides disease detection, growth-stage prediction, and treatment recommendations. The proposed integrated framework can support farmers and agricultural experts in making timely and accurate disease management decisions.

Introduction

India, being an agrarian economy, depends heavily on agriculture, with rice (paddy) serving as a staple crop for a large portion of the population. However, paddy cultivation is significantly affected by leaf diseases such as blast, bacterial leaf blight, brown spot, and sheath blight, which reduce yield and cause economic losses. Early detection of these diseases is essential, as traditional manual inspection methods are time-consuming and often inaccurate. To address this issue, this study proposes a deep learning–based decision support system for paddy leaf growth stage prediction, disease classification, and treatment recommendation. The system uses a neural....

Protocol

The following protocol illustrates the process of paddy leaf disease detection and treatment recommendation system building and deployment step by step. The steps taken need to be used consecutively to replicate the findings presented in this paper.

Dataset preparation

Paddy leaves stage dataset: This data was based on PaddyNet4. The scene comprises 560 images as classified under four different development stages, namely: Stage 2, Stage 3, Stage 4 and Stage 5. The classification depends on the color and maturity of the paddy leaves, which depend on the concentration of nitrogen, among other factors....

Results

Stage classification model

The stage prediction model achieved a training accuracy of 95.88% (loss: 0.1172) and a validation accuracy of 95.24% (loss: 0.1751), indicating that the model is learning consistently and making fair predictions when applied to unseen validation samples. A validation accuracy of 95.24% was achieved, with a validation loss of 0.1751. The model achieved a test accuracy of 94.05 percent and a test loss of 0.1759 on the test data, which supports the robu.......

Discussion

The current paper suggests a novel decision support system which involves the prediction of growth stage of paddy leaves, classification of the diseases with the help of an ensemble of deep learning models (VGG16, MobileNetV2, and ResNet50), and stage-based recommendation of treatment based on a web-based application. The ensemble model got an accuracy of 98.99% in the classification of the model which is higher than that of individual models, as previously reported that models in individual strategy have sensitivity to .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Dataset	Paddy Leaf Stage Dataset (PaddyNet4)	560 images across 4 growth stages (Stage 2–5)
Dataset	Paddy Leaf Disease Dataset	6920 images across 6 disease classes from Kaggle
Dataset	Treatment Dataset	Compiled dataset with disease management practices by growth stage
Preprocessing	Image Resizing	Images resized to 128x128x3
Preprocessing	Normalization	Pixel values scaled to [0,1]
Preprocessing	Label Encoding	Converted categorical labels to numeric
Augmentation	Rotation	Up to 30 degrees
Augmentation	Width/Height Shift	Up to 20%
Augmentation	Zoom	Up to 20%
Augmentation	Shear & Flip	Shearing and horizontal flipping
Model	CNN (Stage Classification)	Lightweight CNN with 3 conv layers
Model	CNN (Disease Classification)	Custom CNN with conv + pooling layers
Model	VGG16	Transfer learning model
Model	ResNet50	Transfer learning model
Model	InceptionV3	Transfer learning model
Model	MobileNetV2	Lightweight transfer learning model
Technique	Ensemble Learning	Average probability voting of 5 models
Technique	Transfer Learning	Using pretrained ImageNet weights
Technique	Dropout Regularization	To prevent overfitting
Training	Optimizer	Adam (learning rate 0.001)
Training	Loss Function	Categorical Cross-Entropy
Training	Batch Size	10 (stage model), 32 (disease models)
Training	Epochs	10 (stage), 20 (disease)
Evaluation	Metrics	Accuracy, Precision, Recall, F1-score
Deployment	Streamlit	Web application for real-time prediction