Soil Conditions an Effective Model Predicts Plant Diseases

Abstract

Plant diseases can cause significant economic losses for farmers and threaten global food security. Traditional approaches for managing plant diseases, such as using chemical pesticides and fungicides, are often expensive, environmentally harmful, and unsustainable. Leveraging the efficiency of soil conditions to predict plant diseases offers a promising alternative to traditional approaches. In this study, we developed a machine learning-based approach to predict the likelihood and severity of plant diseases based on soil conditions. Soil samples were collected from agricultural fields with healthy and diseased plants, and analyzed for various soil properties, including pH, texture, and nutrient availability, as well as microbial communities. A machine learning model was developed using the soil and disease data, and the model's effectiveness was evaluated in a field trial. The results showed that the machine learning-based approach had high accuracy in predicting disease outbreaks based on soil conditions. This research provides valuable insights into the relationship between soil conditions and plant diseases and offers a sustainable and cost-effective approach for managing plant diseases.