Sustainable Farming with AI-Driven Yield Prediction Models and Real-Time IoT Sensor Data

Digital tools, artificial intelligence, and computer vision have revolutionized farming. A fundamental component of agricultural system planning, yield prediction has always depended on statistical models and manual observations— which can distort dynamic environmental conditions and crop variability. This work suggests improving agricultural prediction and decision-making through a computer vision-based yield prediction system comprising IoT sensors, deep learning, and multispectral imaging. It forecasts crop yield with artificial intelligence-driven predictive modeling, machine learning-based feature extraction, and high-resolution picture analysis. The work should address image pre-processing, feature extraction, yield forecasting, and AI-based decision-making in digital crop monitoring. CNNs such as ResNet and EfficientNet and object detection networks such as YOLO and Faster R-CNN measure canopy size, fruit count, and vegetative health, while U-Net and K-means clustering isolate plant structures. In yield prediction, experimental results reveal AI-based models to outperform statistical models. Improving crop stress, nutrient shortage, and disease outbreak projections requires multispectral and hyperspectral imaging. This work investigates AI-driven yield estimate models data scarcity, computational constraints, and environmental variability. The work ends with realistic forecasts for the dependability and scalability of precision agriculture computer vision-driven innovative systems. This work exposes artificial intelligencebased smart farming that offers resource economy and sustainable food production insights and automates yield prediction.