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

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

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

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

AI to Predict Yield in Aeroponics

Reinforcement Learning for Improving Chemical Reaction Performance

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

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

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

METADATA-FARMER ASSISTANCE WEBAPP | AI & ML

Morgan Stanley's Quant Challenge Qualifier Competition

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.

AKFruitYield: AK_SW_BENCHMARKER Azure Kinect Size Estimation & Weight Prediction Benchmarker.

Neural network model for predicting yield per unit area based on the location

Django application for predicting Rice Crop Yield using Random Forest algorithm.

Using an efficient Graph-Based approach, analyze a collection of Arecanut images to determine the quantity of Arecanuts in each cluster. Then, extrapolate the total number of nuts within the entire yield based on the individual counts from each cluster.

This repository provides Lastools and R based scripts for 3D LiDAR data processing and imputation modelling for yield prediction at plot and individual tree levels

An Excel based interface for the soybean model GLYCIM