AgroAware: Smart Farming for a Sustainable Future

DOI

10.2991/978-94-6463-866-0_77How to use a DOI?

Keywords

Precision Agriculture; Plant Disease Detection; Deep Learning; Image processing

Abstract

Smart farming requires smart systems capable of making timely and accurate suggestions for crop choice, fertilizer usage, and detection of plant disease. This article presents AgroAware, a web-based system that utilizes machine learning and deep learning techniques to facilitate precision agriculture. Environmental and soil static factors such as pH, nitrogen, temperature, and humidity are used as inputs to crop and fertilizer prediction models such as Random Forest and Decision Tree algorithms. A Convolutional Neural Network is utilized to classify leaf images with 87% accuracy, recall of 0.86, and F1-score of 85% for plant disease detection. Feature engineering techniques are utilized to normalize and scale the structured data and image data, thereby improving the performance of the models. Image preprocessing methods are utilized to improve feature extraction from the leaves of plants to enable accurate disease classification. The study proposes a modular system integrating recommendation systems and image-based diagnostics into a deployable platform. Future research involves the integration of blockchain technology for the secure and traceable storage of data and more sophisticated neural network architectures to improve the reliability of prediction. The contribution of the research is the integration of machine learning and deep learning methods in an integrated system to improve agricultural productivity and sustainability.

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.

Download article (PDF)

TY  - CONF
AU  - S. Maheswari
AU  - V. Dhilip Kumar
AU  - Punuri Vidhyullatha
AU  - Devaki Venkata Sai Pavan Kumar
AU  - Kota Dharma Teja
PY  - 2025
DA  - 2025/10/31
TI  - AgroAware: Smart Farming for a Sustainable Future
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 955
EP  - 966
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_77
DO  - 10.2991/978-94-6463-866-0_77
ID  - Maheswari2025
ER  -