Neural Harvest Cycle Prediction for Sugarcane Biomass and Sugar Yield
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack the ability to accurately predict harvest cycles for crops, particularly in terms of biomass value, sugar content, and total sugar production, which affects the timing and efficiency of sugarcane harvesting.
Innovation Solution
A system utilizing neural networks to analyze satellite image data, temperature and precipitation measurements, and agronomic parameters to predict biomass and sugar content values, followed by a constraint optimization to determine optimal harvest times and equipment deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine harvest cycles, then the process is simple, but the prediction accuracy of biomass value, sugar content, and total sugar production is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual harvest cycle determination methods with an artificial neural network-based prediction system. The neural network processes satellite image data, weather data, and agronomic parameters to automatically predict biomass value, sugar content, and total sugar production, thereby improving prediction accuracy while substituting complex computational systems for simple traditional methods.
Solution Approach 2:
The patent introduces satellite imagery and weather data as intermediary inputs between the observation of crop growth and the determination of harvest cycles. These intermediaries provide additional information layers that enhance prediction accuracy, allowing the system to account for environmental factors and crop conditions that traditional methods cannot capture.
2Productivity
If harvest timing is determined without precise prediction, then equipment deployment is flexible, but sugar production efficiency is reduced
Solution Approach 1:
The patent performs preliminary prediction of biomass value, sugar content, and total sugar production before the actual harvest decision is made. By using the neural network to forecast these parameters in advance, the system enables optimal harvest timing determination and equipment deployment planning, thereby improving sugar production efficiency and preventing time losses associated with delayed or premature harvesting.
3Measurement precision
If multiple data sources are integrated for prediction, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent employs a universal neural network model that can process multiple types of input data (satellite image data, weather data, agronomic parameters) through a unified architecture. This multi-functional system integrates diverse data sources into a single prediction framework, improving prediction accuracy while managing data processing complexity through a standardized processing approach.
Data Source
AI summary
A method according to an embodiment includes receiving, at one or more processors, satellite image data, temperature measurement data, precipitation measurement data, and one or more agronomic parameters, associated with an agricultural land segment. The method also includes predicting, using the one or more processors and a first neural network, a biomass value associated with the agricultural land segment. The method also includes predicting, using the one or more processors and a second neural network, a sugar content value associated with the agricultural land segment. The method also includes predicting, using the one or more processors, a total sugar value associated with the agricultural land segment. Optionally, the method also includes optimizing harvest dates for a plurality of agricultural land segments that includes the agricultural land segment, based on one or more constraints.