Development of a Predictive Model for Analyzing Crop Losses Due to Natural Disasters

DOI

10.2991/978-94-6463-700-7_9How to use a DOI?

Keywords

Crop Loss Detection; Adam Crayfish Optimization Algorithm (ACOA); True Negative Rate (TNR); True Positive Rate (TPR)

Abstract

- Natural disasters are on the rise and threaten agricultural production, leading to food security issues and poor harvests. Yield loss is difficult to define and quantify because many variables affect plant health precisely. The unreliability of databases makes it difficult to assess losses and implement solutions. This research presents a predictive model leveraging Deep Learning (DL) to enhance the accuracy and efficiency of crop loss detection during natural disasters. The term Global Residual Network (DRN) model is gaining value and has the potential to identify flood statistics in India. Adam Kreifish’s Code of Optimization (ACOA) combines and transforms Kreifish’s Code of Optimization (COA) and Adam’s Optimization with large data sets you generate and present efficiently. This global approach makes it possible to accurately analyze the impact of natural disasters on agricultural productivity across different time periods and geographical areas. This research provides a solid foundation for reducing vulnerability in agriculture and supporting global food security initiatives by bridging the gap between deep learning techniques and complex optimization strategies. The results highlight the importance of integrating predictive analytics into disaster management strategies and pave the way for greater resilience to agricultural disruptions caused by climate change.

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  - Saurabh Shandilya
AU  - Rohit Singh Rajpoot
AU  - Devendar Nath Pathak
AU  - Sachin Jain
AU  - Shalini Singhal
AU  - Priyanka Sharma
PY  - 2025
DA  - 2025/04/19
TI  - Development of a Predictive Model for Analyzing Crop Losses Due to Natural Disasters
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 100
EP  - 106
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_9
DO  - 10.2991/978-94-6463-700-7_9
ID  - Shandilya2025
ER  -