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Artificial Intelligence and Machine Learning in Crop Yield Prediction and Pest Management
Abstract
The application of Artificial Intelligence (AI) and Machine Learning (ML) in agriculture has emerged as a transformative approach to optimize crop yield prediction and enhance pest management strategies. With the growing demand for food production and the challenges posed by climate change, resource constraints, and pest outbreaks, AI-driven solutions provide data-driven insights for precision agriculture. This chapter explores the fundamental concepts of AI and ML in agricultural contexts, highlighting their roles in predicting crop yields, identifying pest risks, and optimizing farm management. Advanced algorithms, such as neural networks, support vector machines, and decision trees, are analyzed for their effectiveness in processing real-time data from IoT sensors, satellite imagery, and weather forecasts. The chapter also discusses integrated pest management (IPM) techniques powered by ML models that allow for targeted pesticide application, reducing environmental impact and costs.
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