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Such results demonstrate that ai can learn underlying pest cycles With the support of machine learning and deep learning algorithms, ai enables precise pest detection,. In this article covered the leveraging ai algorithms and rs data, and how these technologies enable real time monitoring, early detection, and accurate forecasting of pest outbreaks.

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To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. Cial intelligence (ai) has emerged as a transformative tool to reform crop protection strategies These studies highlight the integration of climate data, remote sensing, and machine learning techniques in predicting insect pest outbreaks, contributing to more effective pest.

In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and co2 concentration, using deep learning techniques.

The results showed that the svr model exhibited high prediction accuracy on both training and test sets with a small mean square error (mse) and a high coefficient of determination (r2), which proved the effectiveness of the model in pest prediction. Below is a comparison of top ai disease prediction and ai weather prediction tools for agriculture in 2025, including forecasted accuracy, supported crops, and sustainability impact: In this paper, artificial intelligence (ai) is used for the integration of machine learning (ml), deep learning (dl) and remote sensing technologies in order to increase precision pest prediction and control.