Effects of Weather Parameters on Plant Disease Detection and Prevention

Abstract

Plant diseases pose a serious threat to global food production, causing considerable losses in both yield and quality. The occurrence, development, and spread of these diseases are closely linked to environ- mental and weather parameters such as temperature, humidity, rainfall, wind speed, and solar radiation. Variations in these factors directly affect the life cycles of pathogens and the susceptibility of host plants. Under- standing how weather influences disease dynamics is therefore essential for developing accurate detection and prevention systems. This project aims to explore the relationship between key weather parameters and the emergence of plant diseases using data-driven approaches. The core objective is to utilize machine learning (ML) techniques to analyze large volumes of weather and plant disease data, identify meaningful patterns, and develop predictive models capable of forecasting potential disease outbreaks. By applying algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, the project focuses on determining which models perform best under varying climatic conditions and data complexities. The system integrates meteorological datasets with historical plant disease records to train and validate models that can accurately predict disease risks based on weather trends. The predictive in- sights generated will enable farmers and agricultural experts to take timely preventive actions, such as optimizing pesticide use, selecting resistant crop varieties, and adjusting cultivation practices. Additionally, feature im- portance analysis will be carried out to identify which weather variables most strongly influence disease development, helping to refine future agricultural planning and risk assessment strategies. The expected outcome of this project is a robust, machine learning–based frame- work for plant disease detection and prevention that can significantly reduce crop losses and improve yield quality. By leveraging weather-driven predictive analytics, the project promotes smarter and more sustainable agricultural practices. Ultimately, it demonstrates the potential of artificial intelligence in transforming traditional farm- ing into a more data-oriented, efficient, and resilient sys- tem.

Introduction

Agriculture remains vital to global economies but faces constant threats from plant diseases that reduce crop yield and quality. These diseases are influenced by biological agents (fungi, bacteria, viruses) and environmental factors such as temperature, humidity, rainfall, and wind speed. Unpredictable climate changes have made disease management increasingly difficult. Traditional manual inspection methods are time-consuming and inaccurate, creating a need for data-driven, predictive solutions.

This project proposes a machine learning (ML)-based system to model the relationship between weather conditions and plant disease occurrence. By analyzing historical weather data and plant health information, the system predicts disease risks, enabling early detection and prevention. This approach supports precision agriculture, optimizing pesticide use and minimizing crop loss.

Methodology

The system follows a structured workflow:

1. Data Collection: Gathers weather data (temperature, humidity, rainfall, sunlight), plant images, and disease records from APIs, datasets, or farms.

2. Data Preprocessing: Cleans, normalizes, and augments weather and image data for quality and consistency.

3. Feature Extraction: Identifies key weather parameters and image features (color, texture, leaf shape) affecting disease occurrence.

4. Model Development: Trains ML algorithms—Random Forest, SVM, ANN, CNN, and hybrid models combining weather and image data.

5. Prediction & Alerting: Generates real-time alerts with preventive recommendations (e.g., irrigation or pesticide adjustments).

6. Deployment: Implements a user dashboard for farmers to monitor data, upload images, and receive alerts.

7. Continuous Learning: Updates models with new data for improved adaptability.

Proposed System

The integrated system merges weather analytics, image-based detection, and ML prediction to deliver early warnings and actionable insights. Key applications include:

• Early Detection and Prevention through instant image-based diagnosis.

• Real-Time Monitoring using IoT and remote sensing.

• Disease Surveillance for large-scale agricultural management.

• Decision Support for precision farming.

• Integrated Pest and Disease Management via modular extensions.

• Education and Training for farmers and agronomists.

Feasibility and Design

Using the Iterative Model, the project ensures continuous refinement through multiple development cycles. Feasibility studies confirm that the system is technically, economically, operationally, and schedule-wise viable. It leverages open-source ML tools (TensorFlow, Scikit-learn) and readily available datasets, requiring minimal hardware investment.

The workflow covers stages from data collection and feature extraction to prediction, alert generation, and deployment as a web or mobile application.

Results

A comparative analysis of four ML models—Random Forest, SVM, CNN, and a Hybrid CNN + Weather Model—shows that the Hybrid model achieves the highest accuracy, precision, recall, and F1-score. This demonstrates that integrating visual and environmental features significantly enhances disease prediction reliability.

In essence, this project presents a scalable, AI-driven framework for early plant disease prediction and prevention. By combining machine learning, weather analytics, and image processing, it empowers farmers with real-time, data-informed decisions, paving the way for sustainable and resilient agriculture.

Conclusion

The project successfully demonstrated how weather pa- rameters significantly influence the occurrence and spread of plant diseases. By integrating weather data with image-based disease detection using machine learning, the system provided more accurate and timely predictions. This approach helps farmers and agricul- tural experts take proactive measures, reducing crop loss and improving yield quality. The developed model proved effective in identifying potential disease risks and offering preventive recommendations based on en- vironmental conditions. Overall, the system bridges the gap between traditional farming and smart agriculture by utilizing data-driven insights for sustainable crop management.

References

[1] Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419

Copyright

Copyright © 2025 Shravani Borji, Sneha Brahmane , Mansi Patil, Deepali Shrikhande. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.