International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
DEEPAGRIVISION: GRU-Based Predictive Analytics for Crop Yield and Market Intelligence N. Anjali1, Karne Charan Kumar 2, Basvoju Sharath Kumar3, Garrepalli Abhilash4, Goskonda Rohith Reddy 5 1Assistant Professor, Department of Information Technology, TKR College of Engineering and Technology,
Telangana, India
2345Department of Information Technology, TKR College of Engineering and Technology, Telangana, India
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Abstract - However, modern agriculture is increasingly
planners better understand risks, plan ahead, and make smarter decisions.
beset by uncertainties in terms of weather and environmental changes, which makes precise forecasting more imperative than ever. This project examines the potential of deep learning, and more specifically, a GRU-based model, to enhance crop yield prediction by learning from climatic data, soil data, and past crop yields. The model analyzes raw agricultural data through a sophisticated preprocessing and time-series feature engineering process to produce robust and informative inputs for the GRU model. After training, the model predicts future crop yields and associated values, and its accuracy is verified by RMSE, MAE, and R² measures to ensure its reliability and accuracy. Through the demonstration of the power of AI prediction in agriculture, this project will help create a smarter agricultural ecosystem. Apart from prediction, this project will help in the allocation of resources and the risks associated with climate variability, leading to improved food security.
The project follows step by step process that starts with cleaning and preparing raw data and training the model on historical agricultural data. The model’s performance is then evaluated using standard measures like RMSE, MAE, and R² to ensure the predictions are reliable. Despite the availability of large volumes of agricultural data, most decision-making in farming still relies on intuition or delayed historical analysis. Conventional statistical and machine learning models often fail to capture long-term temporal dependencies and nonlinear interactions among climatic, soil, and market variables. As a result, prediction accuracy degrades significantly under volatile conditions such as irregular rainfall, climate anomalies, and market fluctuations. This gap between data availability and actionable intelligence highlights the need for advanced deep learning approaches capable of extracting meaningful temporal patterns from complex agricultural datasets.
Key Words: Crop Yield Prediction, Deep Learning, GRU (Gated Recurrent Unit), Time-Series Analysis, Climatic and Soil Data, Precision Agriculture, Agricultural Decision Support.
Recurrent neural networks have shown promise in timeseries prediction; however, traditional RNNs suffer from vanishing gradient problems when learning long-term dependencies. Gated Recurrent Units (GRUs) address this limitation through update and reset gates, enabling efficient learning with reduced computational complexity compared to LSTM models. This makes GRU particularly suitable for agricultural forecasting, where datasets are large, multivariate, and sequential in nature. By leveraging GRU architecture, the proposed system achieves a balance between prediction accuracy, training efficiency, and scalability.
1. INTRODUCTION Agriculture plays an important role in our daily lives, but it is also one of the most affected sectors by changes in weather and the environment. Farmers often have to make important decisions without knowing how future conditions will turn out, which can affect food supply, income. Having the right information at the right time can make a big difference. Today, there is more data available than ever before, including weather records, soil information, and past crop yields. The main challenge is turning all this data into something useful. This project focuses on doing exactly that by using modern deep learning approach, specifically a GRUbased model, to predict crop yields more accurately.
1.1 GRU Model Development The objective of this project is to create an effective GRU forecasting model that is capable of comprehending the patterns of crop yields and prices. The GRU model is able to analyze the data and learn the patterns that affect crop yields. The use of the GRU model is effective because it is able to provide accurate results compared to other models.
By studying patterns in climate and soil data over time, the model can be trained relationships that are difficult to capture with traditional methods. This can helps farmers and
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The core component of this project is the forecasting model that is capable of analyzing data in order to provide future
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