Cloud-Connected Grain Harvester Monitoring for Real-Time Loss Tracking
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Solution Overview
Problem
Current harvesting machines lack real-time information capabilities, leading to issues such as unknown fuel consumption, productivity, and grain loss, which affects financial transactions and trust among stakeholders.
Innovation Solution
A harvesting system equipped with sensors connected via a Controller Area Network (CAN) to an electronic control unit (ECU), transmitting data to the cloud for remote access through a mobile or web application, providing real-time data on grain levels, fuel usage, and system parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time monitoring systems with sensors and ECUs are added to harvesting machines, then information transparency and trust among stakeholders are improved, but device complexity and cost increase
Solution Approach 1:
The ECU serves multiple functions: it collects data from various sensors (fuel level, grain level, speed, productivity), processes this information, communicates with the cloud server, and controls display elements. This multi-functionality consolidates what could be multiple separate systems into a single integrated unit, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The cloud server acts as an intermediary between the harvesting machine and stakeholders. Instead of requiring direct complex connections between all components and all users, the cloud server receives data from the ECU and distributes relevant information to farmers, brokers, and other stakeholders through mobile applications, simplifying the information architecture.
2Measurement precision
If multiple sensors and ECUs are integrated for comprehensive monitoring, then measurement precision of various parameters is improved, but device complexity increases
Solution Approach 1:
Multiple sensing functions (fuel level detection, grain level detection, speed sensing, productivity measurement) are merged into a single ECU that processes all inputs. This consolidation maintains the precision of individual measurements while reducing the number of separate control units needed in the system.
Solution Approach 2:
The ECU is designed as a universal control unit that handles diverse measurement tasks: it reads analog signals from fuel and grain level sensors, processes digital signals from speed and productivity sensors, and performs calculations for fuel consumption, grain loss, and productivity metrics all through one device.
3Ease of operation
If real-time data transmission to cloud is implemented, then remote access and transparency are improved, but use of energy and communication infrastructure requirements increase
Solution Approach 1:
Instead of continuous data transmission, the system uses periodic updates where the ECU transmits collected data to the cloud server at intervals. This approach maintains the ability to provide remote access to stakeholders while significantly reducing energy consumption compared to continuous streaming of all sensor data.
Data Source
AI summary
The present disclosure relates to a harvesting system for variety of grain crops. The harvesting system comprises a harvester control unit, a grain loss monitoring GLM-ECU, a cloud communication interface ECU, a plurality of sensors, a controller area network (CAN), a buzzer/alarming device, a display unit, a cloud and a mobile application. The GLM-ECU is configured to process signals from a sieve sensor and a straw walker sensor. The harvester control unit is configured to process signals received from the plurality of sensors and the cloud communication interface ECU serves as an interface to interchange the data between the harvester control unit and the cloud . The harvester system results in increase in productivity and trust among the stake holders by virtue of real-time information sharing.