.../Web Application...
Crop Element
A Flask REST API backend serving two ML models — Naive Bayes crop recommendations and an Inception v3 disease detector (~87% accuracy) — consumed by a React frontend.
const project = {
title: "Crop Element",
category: "Web Application",
stack: ["Python", "Flask", "React", "REST APIs"],
}
.../overview...
Crop Element is a Flask REST API backend that serves machine-learning models to a React frontend — a Naive Bayes classifier for crop recommendations and an Inception v3 transfer-learning model for plant disease detection (~87% accuracy). I designed the API contracts with input validation, structured response formatting, and fallback handling for network failures and model inference timeouts, so growers get reliable results instead of opaque errors.
Problem
The product needed reliable recommendation and disease-detection workflows so growers could act on data instead of manual intuition.
Role
Backend-focused full-stack engineer (university project) — designed the Flask API and ML-serving layer.
Implementation highlights
- 01Designed and implemented scalable REST API endpoints serving Naive Bayes crop recommendation and Inception v3 disease detection models.
- 02Built API contracts with proper error handling, input validation, and response formatting.
- 03Added fallback handling for network failures and model inference timeouts.
Outcomes
- ›Delivered plant disease detection at ~87% accuracy via the Inception v3 transfer learning model.
- ›Provided a single API layer powering both crop recommendation and disease analysis workflows.
- ›Enabled the React front-end to consume reliable, well-formatted ML inference results.