Crop Prediction Using Deep Neural Networks

Abstract

This project introduces a web application based on deep learning that suggests the three most appropriate crops for planting given various factors like temperature, humidity, soil pH, and rainfall. Based on a Multi-Layer Perceptron (MLP) model trained on agricultural data, the system evaluates user inputs and predicts crop suitability along with confidence probabilities. The backend is built with Python and Flask, and the frontend is created with HTML, CSS, and JavaScript. This project supports precision agriculture by improving yield and enabling smart decisions.

Introduction

1. Introduction

Recent advancements in machine learning (ML) and deep learning (DL) are transforming agriculture, particularly in crop prediction. Choosing the right crop based on environmental conditions (soil pH, temperature, humidity, rainfall) remains a major challenge for farmers. To address this, the project presents a web-based platform that uses deep learning to recommend the top three crops suitable for a given environment, ranked by confidence scores.

2. Proposed System

• Core Technology: Uses a Multi-Layer Perceptron (MLP), a type of Deep Neural Network (DNN), trained on synthetic environmental data.

• User Interface: Built with HTML, CSS, and JavaScript, making it user-friendly for all levels of tech proficiency.

• Backend: Developed in Python using Flask, which connects the user input to the MLP model.

• Deployment: Hosted on Render, ensuring internet accessibility and scalability.

3. Features & Advantages

• Provides three ranked crop suggestions rather than a single output.

• Uses probabilistic predictions (via softmax) to help farmers make informed, data-driven decisions.

• Promotes smart and sustainable agriculture by matching crops to local environmental data.

4. Comparison with Existing Systems

• Traditional models (like SVM, KNN, Decision Trees) offer limited single-label predictions and lack ranking mechanisms.

• Existing systems struggle to capture complex, non-linear relationships between environmental inputs.

• The proposed model improves on this by leveraging DNNs' ability to model such relationships and offer ranked, confidence-based predictions.

5. Machine Learning Technologies

• DNN (Deep Neural Networks): Mimic human cognitive processing, suitable for learning complex data patterns.

• MLP (Multi-Layer Perceptron): A fully connected neural network using ReLU for hidden layers and Softmax for output. Trained using Stochastic Gradient Descent and cross-validation for generalization and robustness.

6. System Architecture

• Data Generation: Synthetic data simulating real-world agricultural conditions (temperature, pH, humidity, rainfall).

• Data Preprocessing: Cleaning and normalization, followed by training/testing split.

• Model Structure:

  • 3 hidden layers (32-32-16 neurons), ReLU activation

  • Output layer with Softmax for top 3 predictions

• Prediction Pipeline: User inputs environmental values → Flask backend processes input → Model returns ranked crop suggestions with confidence scores.

7. Web Application & Deployment

• Frontend: Interactive, built with web technologies (HTML/CSS/JS).

• Backend: Flask manages model communication and logic.

• Deployment: Render cloud service integrates with GitHub for real-time updates and access from any internet-enabled device.

8. Results

• System takes input (temperature, pH, rainfall, humidity) and outputs:

  • Top 3 crop recommendations

  • Confidence scores for each crop

• Demonstrates accuracy, speed, and user accessibility across test scenarios.

9. Future Enhancements

• Real-time sensor integration for dynamic weather-based predictions.

• Incorporation of historical yield data for regional specificity.

• Market demand analysis for economically beneficial crop suggestions.

• Seasonality and crop rotation support for sustainable long-term farming.

Conclusion

The crop prediction project was successfully designed and implemented using a deep learning model. By taking key environmental inputs such as temperature, humidity, rainfall, and soil pH, the system can accurately predict the top three most suitable crops for cultivation, along with their respective probability scores. The web-based interface ensures the ease of use and allowing users to input data and instantly receive meaningful predictions. This tool can assist farmers, agricultural officers, and researchers in making the data-driven decisions for sustainable efficient crop planning. Overall, the project demonstrates the practical use of Deep Learning Techniques in agriculture and highlights potential of AI-powered solutions in addressing real-world challenges.

References

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Copyright

Copyright © 2025 Dr. N. Usha Rani, Ramireddy Venunath Reddy, Banka Jyothsna, Lanka LakshmiSivani Reddy, Avvaru Pranavi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.