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CropWise – AI-Based Crop Recommendation System

Project Overview
  • Project Title: CropWise – AI-Based Crop Recommendation System
  • Domain: Agritech
  • Technology Stack: Java 17, Spring Data JPA, MySQL, HTML, CSS, React
  • Duration: 3 Weeks
Project Description

CropWise is an intelligent web application that recommends the most suitable crops to cultivate based on soil characteristics, environmental conditions, and historical agricultural data. By leveraging Machine Learning models, the system analyzes input parameters like soil type, pH, nutrients, rainfall, temperature, and location to suggest crops that will maximize yield and minimize risk. It is especially helpful for small and medium-scale farmers aiming for optimized, data-driven farming.

Key Objective
  • Use machine learning to predict the best-suited crops for a given piece of land.
  • Help farmers make informed decisions based on soil, climate, and seasonal factors.
  • Maximize yield and profitability by recommending high-performing crops.
  • Reduce crop failure risk by avoiding poor crop-soil combinations.
  • Provide an easy-to-use platform accessible on web and mobile.
  • Offer detailed insights for each recommended crop (e.g., care instructions, growth period).
  • Integrate with a larger Smart Farm Management System or function as a standalone module.
Core Feature
  • Input form for parameters: N, P, K, pH, rainfall, temperature, humidity, soil type
  • Real-time crop prediction using a trained ML model (e.g., Random Forest)
  • Information dashboard for each recommended crop
  • Map-based location input for weather and soil zone auto-fill
  • Crop-wise profitability estimator
  • Historical analysis of crop success in the region
  • Admin portal to update crop database and model inputs

Tools & Technologies Used

Category Tools / Technologies
Language Java 17
Framework SpringBoot
ORM Spring Data JPA
ML Model Python (Random Forest / Decision Tree using Scikit-Learn or TensorFlow)
ML Integration ML model exposed via Flask/FastAPI and consumed by Spring Boot via REST
Database MySQL (main database), Redis (cache), MongoDB (optional for IoT logs)
Authentication & Security Spring Security + JWT + OAuth2
API Documentation Swagger / SpringDoc OpenAPI
Logging & Monitoring Log4j, Spring Boot Actuator
FrontEnd React.js
Visualization Chart.js / Recharts
File upload/Downloads Apache POI / JasperReports
Building Tools & Dependencies Maven, Git, Jenkins
Testing Mockito, Postman (API Test)
Cloud and Deployment Docker, AWS EC2, RDS, S3, CloudFront

Prerequisites

💻 System Requirements
  • Operating System: Windows / Linux / macOS
  • Minimum 8 GB RAM (16 GB recommended for model training)
  • Java JDK 17 and Python 3.9+ installed and configured
  • Node.js with npm/yarn (for React frontend)
  • Docker (for deployment and environment setup)
  • MySQL and Redis installed locally or connected via cloud (e.g., AWS RDS)
📦 Software Dependencies
  • Spring Boot (for backend APIs)
  • Spring Data JPA (ORM)
  • React.js (frontend interface)
  • Python with Scikit-Learn / TensorFlow (for ML model)
  • Flask or FastAPI (for exposing ML model via REST)
  • Apache POI / JasperReports (for exporting data or reports)
  • Swagger / SpringDoc OpenAPI (for documenting REST APIs)
  • Chart.js / Recharts (for visualizing prediction insights)
🧰 Tools & Services
  • IDE: IntelliJ IDEA / Eclipse (Java), VS Code / Jupyter (Python)
  • Testing Tools: JUnit, Mockito, Postman (for API testing)
  • Version Control: Git, GitHub
  • CI/CD: Jenkins (automated build pipeline)
  • Cloud & Hosting: AWS EC2 (servers), RDS (database), S3 (file storage)
🌐 Technical Knowledge
  • Understanding of Machine Learning algorithms (Random Forest, Decision Tree)
  • Experience with Flask/FastAPI for RESTful service of Python models
  • Knowledge of React + REST API integration
  • Familiarity with data visualization libraries (e.g., Chart.js, Recharts)
  • Basics of crop science, soil parameters, and agriculture data
  • Working with Docker and cloud deployment practices
  • TechnologyJava
  • TypeWeb Application
  • Duration3 weeks
  • ModeOnline/Offline
  • CertificateYes
  • Project ReviewIncluded
  • Doubt SupportLive & Chat Support
  • Career SupportResume & Interview Tips
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