Abstract: Agriculture is the pillar of world food security, but it is confronted with challenges like climate fluctuation, soil erosion, pest attacks, water shortages, and unstable market prices. AgriPulse is a smart agriculture platform pow-ered by AI that combines Crop Recommendation, Yield Forecasting, Plant Disease Detection, Soil Health Monitoring, Weather Forecasting, Market Connectivity, and AI-based Decision Support to improve agricultural productivity and sustainability. This system utilizes Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), and Ex-plainable AI (XAI) to offer real-time insights as well as predictive analytics. AgriPulse uses Random Forest, Genetic Algorithms, and Support Vector Machines (SVMs) for classification of crops as well as the prediction of yields with high accuracy in suggesting the most appropriate crops according to soil and weather factors. Plant disease detection system based on deep learning using CNNs, GANs, and Self-Supervised Learning (SSL) guarantees early detection of plant infections, minimizing losses from pests and diseases. IoT-based soil health monitoring system continuously monitors moisture, pH, and nutrients, maximizing fertilizer usage and irrigation management. Agri Pulse also has an AI-based market prediction module, with Time Series Forecasting (ARIMA) and Regression models, to forecast crop prices and enable farmers to make informed sales decisions. In addition, weather prediction algorithms examine mete-orological data to offer early warnings of unfavourable conditions to help farmers manage risks. The platform is ac-companied by an AI chatbot that provides localized, personalized recommendations in local languages for ease of access and use. By combining precision agriculture technology, Agri Pulse seeks to optimize crop production, im-prove resource management, lower environmental footprint, and enhance farmers' connectivity with markets. Through this integrative strategy, stakeholders are empowered with data-driven decision-making, leading to a sustainable and resilient food industry future.

Keywords: Machine Learning (ML) in Agriculture, Deep Learning (DL) for Crop & Disease Prediction, Crop Recom-mendation System, Yield Prediction, Soil Health Monitoring, Precision Farming, AI for Plant Disease Diagnosis, Pest Detection using Computer Vision, AI-powered Market Intelligence, Crop Price Forecasting, Time Series Forecasting (ARIMA, LSTM), Remote Sensing in Agriculture, AI-based Weather Prediction, Climate-Smart Agriculture, Self-Supervised Learning (SSL) for Pest & Disease Identification, Automated Decision Support Systems, AI-powered Agri-cultural Chatbots.

| DOI: 10.17148/IARJSET.2025.12220