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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
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.
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.
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
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.