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vineyards-yield-dataset

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train · 926 rows

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PAULSEN	1	2.5	0.268429	0.009071	0.002229	0.293064	0.035556	-0.008215	0.312704	0.040881	-0.006756	0.200864	0.008513	-0.003592	0.235103	0.042101	-0.011339	0.261216	0.050699	-0.009254	0.15497	0.012977	0.001822	0.174675	0.032387	-0.00681	0.193421	0.038215	-0.006111	-0.139122	0.008497	-0.00179	-0.11088	0.034044	-0.009	-0.088892	0.042122	-0.007585	-0.156941	0.014056	0.006375	-0.156044	0.013303	-0.000171	-0.144006	0.021299	-0.002884	-0.049271	-0.068479	-0.039411	-0.056485	-0.001795	-0.061977	0.044138	0.752156	0.641986	0.782622	0.403182	0.573208	0.27196	5.670067	1.070963	-0.27604	6.90574	1.596763	-0.785736	32.461599	91.227125	413.033505	633.616272	1,259.963318	1,377.213837	29.399132
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PAULSEN	1	2.5	0.21098	0.015849	-0.008312	0.233489	0.030425	-0.008185	0.263635	0.050991	-0.00941	0.185365	0.015346	-0.008922	0.210077	0.02937	-0.008416	0.24105	0.051471	-0.009801	0.126397	0.014569	-0.006595	0.140232	0.024443	-0.005648	0.164572	0.041292	-0.007305	-0.145467	0.009981	-0.000683	-0.133585	0.017053	-0.003763	-0.112325	0.034367	-0.006042	-0.151731	0.0083	0.004475	-0.154895	0.008397	0.001762	-0.146437	0.018803	-0.001661	-0.045019	-0.049426	-0.02767	-0.023764	0.00633	-0.041902	0.045076	0.777363	0.657921	0.803142	0.430603	0.581935	0.274912	5.788141	1.200126	-0.348952	7.094805	1.734347	-0.842968	35.093367	93.707648	407.216377	650.616119	1,307.372314	1,427.669952	30.748834
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PAULSEN	1.1	2.55	0.209433	0.008186	0.001123	0.221693	0.015535	-0.003384	0.221358	0.012892	-0.000864	0.164786	0.006571	-0.000093	0.182613	0.019969	-0.005204	0.190346	0.019839	-0.003297	0.116346	0.01509	0.002674	0.124554	0.018569	-0.002588	0.126781	0.015709	-0.001265	-0.163893	0.00784	-0.001681	-0.141592	0.024217	-0.006454	-0.134499	0.022767	-0.003533	-0.186466	0.0112	0.002502	-0.172979	0.020571	-0.004374	-0.15788	0.027673	-0.004749	-0.024519	-0.035655	-0.016416	-0.044601	-0.026974	-0.026473	0.005216	0.755528	0.636983	0.77757	0.417335	0.5647	0.267131	5.589778	1.087096	-0.311271	6.840813	1.61888	-0.79678	33.850745	91.376749	398.388898	627.637543	1,249.714417	1,362.654053	29.573044
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PAULSEN	1.1	2.55	0.221366	0.010631	-0.002772	0.236109	0.01865	-0.004453	0.236223	0.015613	-0.001272	0.164802	0.007039	-0.000707	0.181993	0.019672	-0.004937	0.18826	0.018564	-0.002887	0.122471	0.012832	0.001971	0.134798	0.021256	-0.003878	0.138751	0.018432	-0.001989	-0.132561	0.011347	-0.004492	-0.108936	0.026112	-0.007166	-0.098902	0.025906	-0.004344	-0.205566	0.015939	0.006887	-0.192751	0.022362	-0.003741	-0.181852	0.02425	-0.00356	-0.029485	-0.034382	-0.024654	-0.04725	-0.025629	-0.028559	0.014992	0.755528	0.636983	0.77757	0.417335	0.5647	0.267131	5.589778	1.087096	-0.311271	6.840813	1.61888	-0.79678	33.850745	91.376749	398.388898	627.637543	1,249.714417	1,362.654053	29.573044
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PAULSEN	1	2.5	0.245643	0.012346	-0.00441	0.271085	0.032048	-0.008496	0.288634	0.037166	-0.006465	0.196958	0.012126	-0.00606	0.228359	0.037409	-0.010389	0.25101	0.044625	-0.008267	0.139708	0.014257	-0.001577	0.159738	0.029862	-0.006938	0.175507	0.034148	-0.005591	-0.1399	0.009643	-0.001788	-0.113234	0.033165	-0.00857	-0.09593	0.037227	-0.006357	-0.164087	0.007822	0.003257	-0.161214	0.016375	-0.001522	-0.145881	0.02782	-0.00402	-0.050883	-0.062803	-0.040059	-0.053332	-0.005746	-0.053585	0.042816	0.777363	0.657921	0.803142	0.430603	0.581935	0.274912	5.788141	1.200126	-0.348952	7.094805	1.734347	-0.842968	35.093367	93.707648	407.216377	650.616119	1,307.372314	1,427.669952	30.748834
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PAULSEN	1	2.5	0.23515	0.011805	-0.004565	0.264978	0.040451	-0.010477	0.294223	0.053355	-0.009728	0.198908	0.009961	-0.004918	0.230171	0.038929	-0.010573	0.260145	0.053425	-0.00999	0.134432	0.013447	-0.000917	0.154501	0.031098	-0.007007	0.179922	0.044674	-0.007977	-0.145297	0.010571	-0.00131	-0.120734	0.030718	-0.00774	-0.097891	0.041526	-0.007486	-0.156319	0.009961	0.004363	-0.155696	0.009603	-0.000102	-0.141179	0.023559	-0.003398	-0.059656	-0.062526	-0.040137	-0.049127	-0.001246	-0.057035	0.055413	0.777363	0.657921	0.803142	0.430603	0.581935	0.274912	5.788141	1.200126	-0.348952	7.094805	1.734347	-0.842968	35.093367	93.707648	407.216377	650.616119	1,307.372314	1,427.669952	30.748834
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PAULSEN	1.2	2.5	0.303778	0.011862	-0.002312	0.32067	0.022818	-0.005711	0.330636	0.024917	-0.003696	0.216152	0.016369	-0.008536	0.240023	0.02795	-0.007836	0.257563	0.033988	-0.006337	0.181234	0.010785	-0.001776	0.198046	0.023577	-0.005635	0.211629	0.028539	-0.004583	-0.14952	0.019002	-0.0089	-0.123619	0.030765	-0.008308	-0.105833	0.036071	-0.006468	-0.166788	0.01085	0.001618	-0.172621	0.011356	0.00201	-0.163828	0.018458	-0.001681	-0.033784	-0.047742	-0.033625	-0.051802	0.011666	-0.046069	0.038106	0.754695	0.646204	0.787636	0.404384	0.576402	0.273291	5.781255	1.16513	-0.333801	7.067478	1.688234	-0.826172	32.614023	92.460913	417.578	648.092163	1,298.406464	1,419.855835	29.615483
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PAULSEN	1	2.5	0.264421	0.008443	0.000871	0.284476	0.0282	-0.006557	0.299902	0.032588	-0.005403	0.199806	0.011579	-0.00575	0.229573	0.035291	-0.009771	0.252353	0.043744	-0.008162	0.155469	0.012166	0.00171	0.171778	0.027098	-0.005549	0.186906	0.031563	-0.004995	-0.1637	0.012949	-0.004807	-0.136396	0.031648	-0.008551	-0.115508	0.039833	-0.007339	-0.168585	0.014905	0.006501	-0.169819	0.013323	0.000572	-0.155629	0.024203	-0.003228	-0.04011	-0.059534	-0.032617	-0.054608	0.002468	-0.052498	0.039755	0.752156	0.641986	0.782622	0.403182	0.573208	0.27196	5.670067	1.070963	-0.27604	6.90574	1.596763	-0.785736	32.461599	91.227125	413.033505	633.616272	1,259.963318	1,377.213837	29.399132
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Check out the documentation for more information.

Yield Prediction Model - Open Source Demo

Overview

This project demonstrates machine learning models to predict vines yield at harvest time using remote sensing data, weather information, soil properties, and agronomic attributes.

The models predict:

TCH (Tons of Grapes per Hectare): Vines yield at harvest

Files

training_features_anonymized.csv - Dataset for model training (926 rows × 589 columns)
BuildModels_open_source.ipynb - Self-contained notebook for training prediction models

BuildModels_open_source.ipynb - Quick Start Guide

What It Does

This notebook trains machine learning models to predict vines yield (TCH) at harvest time.

Input

File: training_features_anonymized.csv (926 harvest observations)
Features: Satellite data, weather, soil properties, and crop characteristics

Process

Load and prepare data
- Read CSV file
- Encode categorical variables (variety, rootstock type)
Train models using Leave-One-Season-Out Cross-Validation
- For each season: train on all other seasons, test on held-out season
- Algorithm: LightGBM with 31 leaves, 100 trees
- Remove outliers: TCH (0.1-60 tons/ha)
Evaluate performance
- Calculate metrics: RMSE, MAE, R², MAPE
- Generate scatter plots and feature importance charts
Save final models
- Train on complete dataset
- Export as .joblib files for future use

Output Files

tch_model.joblib - Yield prediction model
tch_encoders.joblib - Label encoders for categorical variables

Feature Set Used

Weather + Soil + Extra:

5 satellite spectral indices (NDVI, EVI, VARI, NDRE, NDWI) × 42 time steps = 210 features
Weather time series: precipitation, temperature, degree days (28 features)
Soil properties: clay, sand, nitrogen, at 4 depths (25 features)
Agronomic: variety, age, cut cycle, day of year (4 features)
Extra: rootstock type, spacing, coordinates (5 features)
Total: ~272 features

Requirements

numpy>=1.21.0
pandas>=1.3.0
scikit-learn>=1.0.0
lightgbm>=3.3.0
matplotlib>=3.4.0
seaborn>=0.11.0
jupyter>=1.0.0
joblib>=1.0.0

Usage

Installation

# Install dependencies
pip install numpy pandas scikit-learn lightgbm matplotlib seaborn jupyter joblib

Running the Notebook

# Navigate to the notebook directory
cd open_source_model/

# Launch Jupyter
jupyter notebook

# Open BuildModels_open_source.ipynb and run all cells

Using the Trained Models

import joblib
import pandas as pd

# Load models and encoders
tch_model = joblib.load('tch_model.joblib')
tch_encoders = joblib.load('tch_encoders.joblib')

# Prepare your data (must have the same features)
# X = pd.DataFrame(...)  # Your feature data

# Make predictions
tch_predictions = tch_model.predict(X)

Downloads last month: 5

Size of downloaded dataset files:

6.16 MB

Size of the auto-converted Parquet files:

4.38 MB

Number of rows:

926

Models trained or fine-tuned on AGRARIAN/vineyards-yield-dataset

AGRARIAN/vineyards-yield-model

Time Series Forecasting • Updated Dec 12, 2025