Multimodal Crop-Field Sensing for Real-Time Soil Organic Carbon Estimation
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Solution Overview
Problem
Traditional methods for estimating soil organic carbon (SOC) are time-consuming, laborious, and fail to consider temporal variations and farm-specific parameters, leading to inaccurate nutrient assessments that can impact crop health and yield.
Innovation Solution
A system and method for real-time SOC estimation using multimodal sensing, involving image analysis and geo-spatial data to identify carbon-nitrogen (C—N) relationship segments, which account for tillage, crop remnants, and crop maturity stages, enabling accurate and efficient SOC estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional laboratory methods are used for soil organic carbon measurement, then measurement accuracy is improved, but time consumption and labor requirements increase
Solution Approach 1:
The patent replaces traditional mechanical/chemical laboratory analysis methods with an optical sensing system using mobile devices. The system captures images of crop fields and uses image processing algorithms to estimate soil organic carbon levels, eliminating the need for physical soil sampling and laboratory analysis while achieving comparable accuracy through remote sensing technology.
Solution Approach 2:
The patent creates a digital copy of the crop field through imaging instead of physically analyzing soil samples. By capturing visual information of the crop canopy and using machine learning models to infer soil properties from these images, the system replicates the measurement function without requiring physical contact with or transport of soil materials to laboratories.
2Loss of time
If traditional contactless SOC estimation methods are used, then time consumption is reduced, but measurement accuracy deteriorates due to lack of consideration for temporal variations and farm-specific parameters
Solution Approach 1:
The patent implements a dynamic estimation system that adapts to temporal variations in crop growth and soil conditions. The machine learning models are trained to recognize and account for changes in crop canopy characteristics throughout the growing season, allowing the system to provide accurate SOC estimates at different time points rather than relying on static historical data.
Solution Approach 2:
The patent tailors the estimation algorithm to specific farm conditions by incorporating farm-specific parameters such as crop type, soil texture, and management practices. The system analyzes local image characteristics and adjusts the interpretation accordingly, rather than applying a uniform estimation method across all fields, thereby maintaining high accuracy for diverse agricultural contexts.
3Reliability
If frequent soil testing is performed to improve SOC measurement accuracy, then measurement reliability is improved, but productivity and ease of operation deteriorate
Solution Approach 1:
The patent enables farmers to independently conduct SOC measurements using their own mobile devices without requiring professional laboratory services. The system processes images through automated machine learning algorithms that provide immediate results, eliminating the need for farmers to schedule and wait for external testing services while maintaining reliable measurements.
Solution Approach 2:
The patent allows for continuous monitoring of SOC levels throughout the growing season by enabling frequent, on-demand measurements. Farmers can capture images and receive estimates in real-time without interruption to their agricultural activities, unlike traditional methods that require scheduling and long waiting periods between measurements.
Data Source
AI summary
This disclosure relates generally to method and system for real time estimation of soil organic-carbon with multimodal sensing of crop fields. Estimating soil organic carbon is affected by various factors on the farm with available nutrients in the soil and agricultural management practices followed by farmers. Existing soil testing methods are time consuming and complex. The method initially computes a soil nitrogen level of the target crop field based on geo-spatial profile of neighboring crop field. Here, a plurality of features comprising a tillage, one or more crop remnants, and a crop maturity stage from the plurality of digital images are identified to determine at least one of a carbon-nitrogen relationship segment comprising a C—N segment 1, a C—N segment 2 and a C—N segment 3 of the target crop field. Based on the C—N segment, an organic carbon level of the target crop field is estimated.