Predictive Crop Health Monitoring for Early Plant Intervention
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Solution Overview
Problem
Current crop monitoring systems face challenges in predicting crop quality and yield, detecting pests and diseases early, and providing precise intervention due to reliance on human scouts, which can spread diseases and are subjective, and existing sensor systems are often cumbersome, costly, and lack real-time sensitivity.
Innovation Solution
A multi-sensor device and platform that captures and transmits plant-related data without physical contact, using physiological, surface analysis, and chemical sensors, with a control unit, location tracking, and communication interface, allowing for autonomous movement and data analysis to predict future health issues and provide timely interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human scouts are used to visually inspect crops, then crop monitoring can be performed, but diseases may spread through physical contact and interpretation varies subjectively
Solution Approach 1:
The patent replaces human scouts with automated imaging devices and computational analysis systems. Machines capture images and use algorithms to detect pests and diseases, eliminating physical contact that spreads diseases while providing consistent, objective measurements across all crops.
Solution Approach 2:
The system creates digital copies of crops through imaging technology. Instead of humans physically examining each plant, the system captures visual representations and analyzes them computationally, allowing multiple assessments without contacting the actual crops.
2Reliability
If human scouts are used for crop monitoring, then crop inspection can be performed, but the speed of covering large areas is limited
Solution Approach 1:
The patent replaces manual inspection with automated imaging systems that can rapidly capture and analyze multiple crops simultaneously. The system processes images computationally, enabling coverage of large areas at speeds impossible for human scouts while maintaining detection accuracy.
3Extent of automation
If sensor systems are used to monitor crops, then data collection can be automated, but the devices are cumbersome and costly
Solution Approach 1:
The patent extracts the essential monitoring function from complex sensor systems and implements it through standard imaging devices. By using conventional cameras and computational algorithms instead of specialized sensors, the system achieves automation with simpler, more affordable components.
4Measurement precision
If visual detection devices are used to identify pests and diseases, then causal factors can be detected, but significant damage may already have occurred
Solution Approach 1:
The patent implements continuous automated monitoring that detects pests and diseases at early stages before visible damage occurs. The system captures and analyzes images regularly, identifying issues in their initial phases and enabling preventive intervention before significant crop damage develops.
Data Source
AI summary
A method includes receiving, using at least one processor, first sensor data pertaining to plant-related parameters of each of multiple first plants over time. The method also includes storing the first sensor data in at least one memory. The method further includes identifying, using the at least one processor, an issue affecting at least one of the first plants. The method also includes analyzing, using the at least one processor, at least some of the stored first sensor data to generate a predictive model associated with the issue. The method further includes receiving, using the at least one processor, second sensor data pertaining to plant-related parameters of each of multiple second plants. In addition, the method includes identifying, using the at least one processor, at least one of the second plants to receive one or more interventions by applying the predictive model to the second sensor data.