g agricultural specialists to properly understand and accept the model’s predictions. Author contributions Arunapriya.R – Problem Statements, Implementation and Testing Dr.S.P.Valli – Results, Conclusion, and Summary. Data availability The datasets used and/or analysed during the current study available and mentioned in below [ https://www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ]. (https:/ www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ). [ https://www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ]. (https:/ www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ). [ https://ww
Open resource ↗kaggle · lines:372-388Unverified paper record
Development of a spontaneous disease diagnosis tool by executing an enhanced convolutional neural network model for citrus fruits and leaves.
Scientific reports · 26 Mar 2026 · 10.1038/s41598-026-40896-7
Abstract
Oranges, mandarins, bitter oranges, and lemons are examples of citrus fruits that make delicious meals and are highly nutritious. Citrus fruits suffer from a variety of infections that affect their yield. The Department of Agriculture wants to increase the production of oranges and lemons. On the other hand, several plant diseases and their advanced stages have impacted production. The quality of fruit influences market value and its financial effect. Therefore, accurate detection of ailments and their severity is crucial for improving the output and market value of oranges and lemons. To automatically evaluate and predict diseases in citrus leaves and fruits, this paper has proposed a modified convolutional neural network (ICNN) model. Python is used to create the ICNN model, and testing is performed using benchmark datasets from various repositories. The research presented here shows that ICNN performs better than traditional deep learning and machine learning models, such as the Convolutional Neural Network (CNN) and K-Nearest Neighbours (KNN). This illustrates how machine learning models require supplementary approaches to extract parameters from data that arrives in non-automated ways. Additionally, to improve the accuracy of their classification or prediction, deep learning models require pre-trained models. As a result, ICNN, an enhanced deep learning model that can automatically predict disease with higher accuracy than other models, represents an advancement over standard CNNs. Compared with KNN and CNN, ICNN achieves 99.69% accuracy.
Plant phenotyping relevance
柑橘の葉・果実の病害と重症度を画像から自動推定するCNN手法を開発し、ベンチマークデータセットで比較評価しており、植物フェノタイピング手法が中心である。
abstractTo automatically evaluate and predict diseases in citrus leaves and fruits, this paper has proposed a modified convolutional neural network (ICNN) model.
abstracttesting is performed using benchmark datasets from various repositories.
abstractCompared with KNN and CNN, ICNN achieves 99.69% accuracy.
Code and data availability
The paper's Data Availability statement explicitly lists three public Kaggle URLs as the datasets used and analysed in the study (citrus/plant leaf disease image datasets). These are paper-specific, publicly accessible image assets directly supporting the phenotyping/disease-classification analysis. The Mendeley URL (3
ts, Conclusion, and Summary. Data availability The datasets used and/or analysed during the current study available and mentioned in below [ https://www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ]. (https:/ www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ). [ https://www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ]. (https:/ www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ). [ https://www.kaggle.com/code/ritzing/plant-disease-detection-using-keras-cnn-model ]. (https:/ www.kaggle.com/code/ritzing/plant-disease-detection-using-keras-cnn-model ). Declarations Competing inter
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