International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 01 | Jan 2026
p-ISSN: 2395-0072
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AI-Powered crop yield forecasting for precision agriculture Mourya S Dasharath,Kritika Vishal , Likith S , Nagendra Prasad P N Guide: Prof. Chetan Ghatage, Asst. Professor, Dept. of CSE, RNSIT ----------------------------------------------------------------------***-------------------------------------------------------------------------
Abstract—Agriculture remains a critical domain in global food security, yet farmers often rely on intuition rather than datadriven decision-making. This research presents an integrated IoT- ML system that leverages real-time soil and environmental data to recommend the top three most suitable crops for a given farmland. Unlike conventional prediction models that depend solely on static datasets, the proposed system fuses real-time sensor values retrieved via Think Speak and atmospheric data obtained from the Google Weather API to deliver dynamic crop recommendations. Soil nutrient parameters (N, P, K), pH, organic matter content, crop cycle duration, and soil texture are entered manually by the user, while temperature, humidity, moisture, and rainfall are automatically fetched from sensor feeds. The Ran- dom Forest Classifier—identified through comparative evaluation against Gradient Boosting and KNN—achieved 100% top-1 and top-3 accuracy on the training dataset and demonstrated superior generalization capability.
The model was deployed using Flask, enabling seamless integration with a web interface where farmers can input soil parameters manually or rely on automated sensor ingestion. The system returns top-3 crop recommendations with probability scores, ensuring explainability and confidence estimation. Real- time soil monitoring, automated weather integration and a user- friendly web interface position this work as an impactful tool toward precision agriculture. Results validate the system’s effect- tiveness in real-world scenarios, achieving accurate predictions for crops such as grapes and roses, demonstrating practical yield estimation capabilities. This work contributes a scalable, low-cost, and sensor-integrated solution capable of empowering farmers with actionable insights to improve agricultural productivity and sustainability.
I. Introduction Agriculture is undergoing rapid digital transformation with the rise of IoT, artificial intelligence, and precision farming methodologies. Despite technological advancements, many farmers still depend on traditional, experience-based decision making, resulting in suboptimal crop selection, inefficient resource usage, and yield inconsistencies. Predicting the most suitable crop for a given farmland requires understanding a combination of soil nutrients, environmental conditions, and historical crop performance—factors that cannot be reliably assessed without computational support. To address these challenges, this work introduces an AI-powered crop rec-commendation system enriched with IoT-driven real-time data ingestion. The motivation behind this project is to reduce guesswork and provide farmers with actionable, data-backed insights. By leveraging machine learning and real-time sensing, the system bridges the gap between raw agricultural data and interpretable recommendations. The system integrates soil nutrient data—Nitrogen (N), Phosphorus (P), Potassium (K), pH, crop cycle duration, soil texture, and organic matter—with atmospheric parameters like temperature, humidity, moisture content, and rainfall. Think Speak IoT feeds were utilized to retrieve real-time soil and climate data, while external rainfall data was acquired dynamically from the Google Weather API. Furthermore, the design prioritizes ease of use by employing a simple yet efficient Flask-based web interface. The platform automatically fetches sensor data while allowing manual over- rides to preserve flexibility in low-sensor-availability regions. The machine learning model, built using Random Forest, processes these inputs to recommend the top three most suitable crops—enabling farmers to make informed choices that maximize productivity. Overall, this project embodies a holistic approach to modern agriculture by combining IoT automation, machine learning, and an intuitive user experience.
II. LITERATURE REVIEW Existing literature highlights the transformative potential of machine learning and IoT in agriculture. Studies such as Kiran et al. (2024) have emphasized the importance of inte- grating weather, soil nutrients, and environmental factors for crop prediction, demonstrating high accuracy using machine learning models such as Random Forest and GRNN. Similarly, Islam et al. (2023) introduced an IoT-enabled soil nutrient monitoring device integrating sensors such as NPK, DHT11, and moisture
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