Harvest Forecasting System Optimizing Machine Resource Allocation
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Solution Overview
Problem
Modern agriculture faces challenges in optimizing the use of harvesting machines for efficient and economic harvests, as existing assistance systems do not adequately support farmers in predicting resource needs and machine settings, leading to suboptimal harvest outcomes.
Innovation Solution
An assistance system with a database and forecasting unit that uses historical, current, and expected harvest data, along with machine data, to create a harvest forecast and determine optimal machine settings for harvesters, considering economic and ecological factors, and provides visualization tools for users to assess and adjust harvest plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If farmers make harvest decisions without advanced prediction tools, then decision-making is simpler, but harvest efficiency and resource optimization deteriorate
Solution Approach 1:
The forecasting unit performs predictions about machine resource requirements and optimal settings before the actual harvest operations begin. By analyzing historical harvest data, current harvest data, and expected harvest data stored in the database, the system pre-determines optimal machine settings and resource allocation strategies, allowing farmers to prepare in advance rather than making decisions reactively during harvest operations
Solution Approach 2:
The assistance system acts as an intermediary between raw harvest data and farmer decision-making. The forecasting unit processes complex data from multiple sources (historical data, current data, expected data, machine data) and transforms it into actionable predictions about machine resource requirements and optimal settings, simplifying the decision-making process while improving harvest efficiency
2Productivity
If farmers use basic resource planning without harvest-specific optimization, then resource allocation is simpler, but machine utilization and harvest outcomes deteriorate
Solution Approach 1:
The system provides localized optimization recommendations specific to each field and harvesting machine combination. The forecasting unit analyzes machine data from specific harvesting machines alongside field-specific harvest data to generate tailored predictions about optimal machine settings and resource requirements, rather than applying generic planning rules that would not optimize machine utilization for specific local conditions
3Loss of time
If farmers manually optimize harvest operations without predictive tools, then operational control is more direct, but time consumption and opportunity costs increase
Solution Approach 1:
The forecasting unit automatically performs predictions about machine resource requirements and optimal settings without requiring manual intervention from farmers. The system autonomously processes historical harvest data, current harvest data, and expected harvest data from the database, applies forecasting models, and generates predictions that reduce harvest campaign time while minimizing opportunity costs
Data Source
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AI summary
The present invention relates to an assistance system (1) for harvesting support during the cultivation of at least one agricultural field (F), wherein the assistance system (1) comprises a database (2) and a forecasting unit (3), wherein the database (2) includes historical harvest data (4), current harvest data (5), and expected harvest data (6) of the at least one agricultural field (F), as well as machine data (7) of at least one harvesting machine (11, 17), wherein the forecasting unit (3), based on a planning specification (8) and depending on the harvest data (4, 5, 6) and machine data (7) stored in the database (2), generates a harvest forecast using a forecasting model (9), which depicts the expected requirement of machine resources for carrying out the harvest on the at least one field (F) according to the planning specification (8), wherein the assistance system (1) is based on the harvest data (4, 5, 6) stored in the database (2),6) and machine data (7) names optimization criteria, and that the forecasting unit (3) determines a machine setting (12) for a harvesting machine (11, 17) based on a given optimization criterion and assigns it to a harvesting machine (11, 17) which is named as available in the machine data (7).