How can Artificial Intelligence (AI) and Machine Learning (ML) algorithms be used to optimize input management (irrigation, fertilizers) in millet-based intercropping systems?
AI and ML can optimize input management by analyzing real-time data like soil moisture, weather patterns, and crop growth to make precise, data-driven decisions. Machine learning models can predict crop stress or yield under varying input levels, while fuzzy logic helps deal with uncertainties, offering flexible irrigation and fertilizer schedules.
In my paper, "Advanced Crop Recommendation System Leveraging Deep Learning and Fuzzy Logic", I addressed similar challenges using a deep learning–based yield predictor (CO-GRU) and a Fuzzy Inference System, which can be adapted to guide input application for intercropping setups like millet-based systems.
Learn more here:Article ADVANCED CROP RECOMMENDATION SYSTEM: LEVERAGING DEEP LEARNIN...
AI and machine learning (ML) algorithms can optimize input management in millet-based intercropping systems by integrating IoT sensor data (e.g., soil moisture, nutrient levels, weather) with historical and spatial datasets to model crop dynamics. Supervised learning models like Random Forest or deep learning architectures like CNNs and LSTMs predict optimal crop combinations, yields, and resource needs, achieving up to 95% accuracy in yield forecasting. These models recommend precise fertilizer and irrigation schedules, optimize row ratios (e.g., 2:1 pearl millet + green gram), and detect pests using computer vision, minimizing resource waste. Explainable AI techniques like SHAP ensure transparent recommendations, while real-time IoT integration enables dynamic decision support via mobile apps, enhancing efficiency and sustainability in intercropping systems.
By analyzing soil health and weather data to predict irrigation and fertilizer needs, these algorithms enable precision farming through real-time monitoring and automated irrigation systems, ensuring optimal crop care. Additionally, AI can simulate different intercropping scenarios to maximize yields and track performance trends to make better decisions. By allocating resources efficiently and providing actionable insights, these technologies enhance sustainability and productivity in millet farming.
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