AgricultureClassical-SupervisedEmerging Standard

Crop Selection and Yield Prediction using Machine Learning Approach

This is like a smart farming advisor that looks at past harvests, weather, and soil data to suggest which crop to plant on a field and how much yield to expect, instead of farmers relying only on experience and guesswork.

9.0
Quality
Score

Executive Brief

Business Problem Solved

Helps farmers and agri-businesses choose the most suitable crop for given conditions and predict expected yield using machine learning models, reducing the risk of poor crop choice and improving planning of inputs, logistics, and financing.

Value Drivers

Higher yield per acre through better crop–environment matchingReduced financial risk from planting unsuitable cropsMore efficient use of fertilizers, water, and other inputsImproved planning for storage, transport, and market timingData-driven recommendations that scale beyond individual farmer experience

Technical Analysis

Model Strategy

Classical-ML (Scikit/XGBoost)

Data Strategy

Structured SQL

Implementation Complexity

Medium (Integration logic)

Scalability Bottleneck

Data quality and coverage across different regions and crop varieties; model performance will vary significantly with local data availability.

Technology Stack

XGBoost(Low)Random Forest(Low)Decision Tree(Low)

Market Signal

Adoption Stage

Early Majority

Differentiation Factor

Academic-style machine learning approach focused specifically on crop selection and yield prediction, likely using interpretable tabular models and agronomic features, rather than broad farm-management platforms.

Part of Application

Agricultural Yield Optimization

109 use cases in this application

Explore More

More in Agriculture→More Classical-Supervised→

Source

https://ouci.dntb.gov.ua/en/works/4wBmv0d7/