CROP YIELD PREDICTION USING DEEPLEARNING

This research paper presented a crop yield prediction system using deep learning techniques. The proposed system analyzes agricultural datasets containing weather conditions, soil parameters, and historical crop yield records in order to estimate future crop production. A Deep Neural Network model was used to learn patterns from historical agricultural data and identify relationships between environmental factors and crop productivity. The experimental results demonstrated that the deep learning model can effectively analyze agricultural data and generate accurate crop yield predictions. One of the major advantages of the proposed system is its ability to process large datasets and capture complex relationships between multiple agricultural parameters. This capability allows the system to provide more reliable predictions compared to traditional statistical methods. The crop yield prediction system can assist farmers, agricultural researchers, and policymakers in improving agricultural planning and resource management. By providing early predictions of crop production, the system helps farmers make better decisions regarding crop selection, irrigation planning, and fertilizer management. Although the proposed system improves prediction accuracy, it is important to recognize that agricultural productivity is influenced by many unpredictable environmental factors. Therefore, the system should be considered as a decision-support tool rather than a fully automated prediction system. Future improvements can include integrating real-time weather data, satellite imagery, and Internet of Things (IoT) sensors for collecting agricultural information directly from farms. These enhancements can further improve prediction accuracy and support the development of smart agriculture systems. Overall, the proposed deep learning-based crop yield prediction system demonstrates how artificial intelligence can be effectively applied to agriculture in order to improve productivity, support farmers, and contribute to sustainable agricultural development.

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