CNN-Based Citrus Disease Diagnosis and Fertilizer Recommendation

DOI

10.2991/978-94-6239-723-1_19How to use a DOI?

Keywords

Citrus limon; Deep Learning; Convolutional Neural Networks; CNN; detection of plant diseases; fertilizer recommendation; smart agriculture

Abstract

Leaf pathologies and nutrient deficiencies have a strong influence on the cultivation of Citrus limon (lemon) and decrease the yield and quality. The paper proposes the decision support system, which utilizes deep learning to detect and recommend fertilizers and diseases automatically. Convolutional Neural Network (CNN) is used in order to assign different images of lemon leaves to 9 categories that can be either diseased or healthy. The image preprocessing and data augmentation are used to increase generalization of the model. According to the anticipated disease, a rule-based module will give the right recommendations (fertilizer or treatment) to facilitate timely agricultural response. Experimental findings show that the classification works well across classes, which proves the efficiency of the offered approach. Nevertheless, the present assessment is restricted to one dataset and does not have an independent test set, which can influence generalizability. The future work will be aimed at the enhancement of evaluation procedures, the inclusion of the real-field information, and the creation of the lightweight models of the real-time precision agricultural use.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tejas Jagannath Yadav
AU  - Sunita S. Dhotre
PY  - 2026
DA  - 2026/07/14
TI  - CNN-Based Citrus Disease Diagnosis and Fertilizer Recommendation
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 207
EP  - 215
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_19
DO  - 10.2991/978-94-6239-723-1_19
ID  - Yadav2026
ER  -