Smart Agriculture 4.0: Precision Crop Disease Identification and Classification Using Advanced AI Techniques

DOI

10.2991/978-94-6463-716-8_45How to use a DOI?

Keywords

Smart Agriculture; Precision Agriculture; Crop Disease Identification; Artificial Intelligence; K-Means Clustering and Improved Alex Net

Abstract

Smart Agriculture 4.0 is focused on accuracy in crop disease diagnosis and classification through the use of the latest advances to revolutionize the way farming is done. This study thus presents a robust framework that makes use of AI tools to enhance agricultural output and minimize losses. This method incorporates state-of-the-art image processing for preprocessing and data gathering, using K-means clustering for efficient segmentation and contrast-limited adaptive histogram equalization (CLAHE) for picture improvement. For feature extraction, the improved AlexNet architecture is utilized, while a model that combines EfficientNet and LSTM for better accuracy and reliability is used for classification. This uses the Python platform for implementation, and measures such as accuracy, recall, precision, and others are used for assessment. For a complete evaluation, finally, the proposed approach is contrasted with current methodologies. By looking to improve agricultural production and sustainability by intelligent, real-time diagnostics this chapter investigates advanced artificial intelligence algorithms for accurate crop disease diagnosis in 4.0 smart Agriculture. Experimental results for achieving the highest specificity, achieved with 70 percent on the learning rate with proposed the method is 0.9919, the high specificity with 80 percent on the learning rate with 0.9944.

Copyright

© 2025 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  - Ruchika Rai
AU  - Pratosh Bansal
PY  - 2025
DA  - 2025/05/26
TI  - Smart Agriculture 4.0: Precision Crop Disease Identification and Classification Using Advanced AI Techniques
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 583
EP  - 598
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_45
DO  - 10.2991/978-94-6463-716-8_45
ID  - Rai2025
ER  -