There are 4 repositories under yield-prediction topic.
ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.
The Crop Management System is a machine learning-based project designed to provide predictions and recommendations for farmers.
AI to Predict Yield in Aeroponics
Python data pipeline to acquire, clean, and calculate vegetation indices from Sentinel-2 satellite image. Package is available only for our clients.
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.
METADATA-FARMER ASSISTANCE WEBAPP | AI & ML
The Crop Yield Prediction System uses machine learning to forecast agricultural yields and provides essential crop information. Integrating weather, soil, and historical data, it offers accurate predictions and supports models like Linear Regression, Random Forest, and Neural Networks.
Django application for predicting Rice Crop Yield using Random Forest algorithm.
AKFruitYield: AK_SW_BENCHMARKER Azure Kinect Size Estimation & Weight Prediction Benchmarker.
Neural network model for predicting yield per unit area based on the location
Simple raw materials and transformations yield calculation symfony 5 app
With the given a set of images of the Arecanuts yield, count the number of Arecanuts available in each bunch and based on the count obtained from each bunch, estimate the total number of nuts available from the yield using efficient Graph Based approach.
CNN for predicting crop yield
A simple implementation for course project. The topic is to predict the fresh weight, dry weight, and leaf area with EfficientNet B7.
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
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.
Source code for a soybean simulation model- GLYCIM
An Excel based interface for the soybean model GLYCIM
Morgan Stanley's Quant Challenge Qualifier Competition
AgTech/AgriTech Data Analytics