ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.

AgriTech is an AI-powered web platform that offers crop recommendations, yield prediction, disease detection, and collaborative tools to empower farmers and promote smart, sustainable agriculture.

The Crop Management System is a machine learning-based project designed to provide predictions and recommendations for farmers.

codes for RS paper: Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal

Python data pipeline to acquire, clean, and calculate vegetation indices from Sentinel-2 satellite image. Package is available only for our clients.

Reinforcement Learning for Improving Chemical Reaction Performance

AI to Predict Yield in Aeroponics

The project aims to create a centralized database of farmers with digital profiles that include all their relevant details. Additionally, the system features an alert system for new schemes and subsidies, as well as a machine learning-based crop recommendation system and disease detection with fertilizer suggestions.

AK_VIDEO_ANALYZER that analyses videos on which to automatically detect apples, estimate their size and predict yield at the plot or per hectare scale using the appropriate simulated algorithms.

Goal of this project was to predict beef carcass 22 yield parameters using image analysis. The code (written in MATLAB, Python) for image processing, feature extraction and multivariate modelling is found in this repository

ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.

An AI-based system that recommends crops, predicts yield and diseases, and provides irrigation guidance using real-time weather and soil data.

Système professionnel de yield management touristique. Basé sur 8+ années d'expérience terrain gérant €15M+ de contrats avec Oberoi, Mercure, TBH Hotels, Hilton. +23% CA, +580K€ profit.

METADATA-FARMER ASSISTANCE WEBAPP | AI & ML

ECE471 Final Project: Pixel-Wise Crop Yield Prediction from County-Wise Labels

Machine-learning pipeline for European cereal yield prediction using FAOSTAT and ERA5 climate indicators, with full EDA, modeling, and SHAP/LIME explainability.

End-to-end AI/ML system for semiconductor manufacturing that predicts test yield, detects process anomalies, and surfaces root-cause signals from high-dimensional sensor data. Built with reproducible pipelines, explainable models, and production-style APIs.

An AI-based crop recommendation and yield prediction system using soil nutrients and climate data. Built with the Crop Recommendation Dataset and simulated yield values, the project covers EDA, feature engineering, classification and regression modeling, weather API integration, and full

A simple implementation for course project. The topic is to predict the fresh weight, dry weight, and leaf area with EfficientNet B7.

Morgan Stanley's Quant Challenge Qualifier Competition