Crop Recommendation System Using Calibrated Satellite and Ground Sensor Data
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Solution Overview
Problem
Agricultural practices face challenges in accurately monitoring crop growth and fertilizer application due to limitations in existing sensor technologies, particularly with satellite imagery being affected by atmospheric conditions and ground-based sensors being time-consuming, leading to potential over or under-application of nitrogen, which affects crop yield and environmental impact.
Innovation Solution
A system combining ground and satellite data using a processor to generate calibrated plant metrics like NDVI, incorporating soil, crop, and climate data to create customized field prescriptions for fertilizer application, and employing a method to calibrate satellite data with ground-based measurements to account for biases and offsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite imagery is used for crop monitoring, then wide-area coverage is achieved, but measurement accuracy deteriorates due to atmospheric conditions
Solution Approach 1:
The patent uses ground-based sensors as an intermediary to calibrate satellite imagery. The ground sensors provide accurate local measurements that serve as reference data to correct atmospheric biases in satellite data, enabling both wide coverage and high accuracy.
Solution Approach 2:
The system changes the calibration parameters by using ground-based measurements to adjust and correct satellite-derived parameters. This involves transforming the satellite data through calibration factors derived from ground truth data to achieve accurate measurements.
2Measurement precision
If ground-based sensors are used for crop monitoring, then measurement accuracy is improved, but time consumption increases
Solution Approach 1:
The system uses ground-based sensors partially - only at selected locations for calibration purposes rather than comprehensive field-by-field monitoring. This partial use of ground sensors provides sufficient calibration data to correct satellite measurements across large areas without the time cost of exhaustive ground surveying.
3Productivity
If high dose of fertilizer is applied to ensure crop potential, then crop yield is improved, but waste and pollution increase
Solution Approach 1:
The system enables local quality management by providing spatially variable fertilizer recommendations based on calibrated NDVI measurements. Different zones within a field receive customized fertilizer rates according to their specific crop needs, eliminating the need for uniform high-dose application and reducing overall fertilizer waste.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides more precise and accurate crop monitoring and fertilizer application, reducing waste and pollution by optimizing nitrogen use, improving crop yield, and enhancing decision-making with real-time, variable-rate application capabilities.
Implementation Method 1
NDVI is based on differences in optical reflectivity of plants and dirt at different wavelengths. Dirt reflects more visible (VIS) red light than near-infrared (NIR) light, while plants reflect more NIR than VIS.
Implementation Method 2
Chlorophyll in plants is a strong absorber of visible red light; hence, plants' characteristic green color.
Data Source
AI summary
In a method of for generating a crop recommendation, a plurality of data sets are received by a computer system from a plurality of disparate data sources, wherein each of said plurality of data sets describes a factor affecting a crop. A benchmark is created by the computer system for each of the data sets which describes how the factor affects the market value of the crop. A model is generated by the computer system which describes the crop based upon each of said benchmarks from the plurality of data sets. A report is then generated by the computer system comprising at least one recommendation to increase the market value of the crop.