Smart Crop: Ensemble Intelligence for Cotton Plant Health Monitoring

DOI

10.2991/978-94-6463-940-7_36How to use a DOI?

Keywords

Ensemble Intelligence; Convolutional Neural Networks; Recurrent Neural Networks; Multimodal Data; Cotton Plant Health Monitoring; Precision Agriculture; Deep Learning; Smart Farming

Abstract

In the current agricultural environment, the prompt and accurate identification of crop diseases is essential for safeguarding food security and sustaining lucrative farming methods. Conventional approaches that depend solely on observation or a singular data source frequently inadequately encompass the intricacies of plant health. This research establishes a Ensemble Intelligence for Cotton Plant Health Monitoring, integrating the advantages of CNNs and RNNs to address existing constraints. The suggested approach involves utilizing convolutional neural networks (CNNs) for spatial characteristics derived from leaf pictures and recurrent neural networks (RNNs) for temporal patterns obtained from environmental time-series data. By integrating these two perspectives, the model provides a comprehensive depiction of disease progression. The predictions of the two models can be integrated by ensemble methods, including weighted averaging and majority voting. This enhances the accuracy and consistency of the diagnostic findings. The research on multimodal datasets encompassed a diverse array of cotton plant species cultivated in various climatic conditions. Our hybrid framework markedly enhances accuracy, precision, recall, and F1-score in comparison to independent CNN and RNN models. This performance enhancement underscores the necessity of integrating data streams from multiple sources to provide a more precise and reliable representation of plant disease. This framework offers farmers an intelligent, data-driven diagnostic system that facilitates rapid decision-making, improves plant health management, and lays the groundwork for future precision agricultural technology.

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  - Chakradhara Rao Gadi
AU  - Venkata Durgarao Matta
AU  - Achuta Sai Ram
AU  - Veeranala Neelima
AU  - Nadimpalli Prashanthi
PY  - 2025
DA  - 2025/12/31
TI  - Smart Crop: Ensemble Intelligence for Cotton Plant Health Monitoring
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 498
EP  - 510
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_36
DO  - 10.2991/978-94-6463-940-7_36
ID  - Gadi2025
ER  -