Multi-legged Robot Adaptive Gait for Crop Scouting
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Solution Overview
Problem
Manual crop scouting in large and diversified agricultural fields is labor-intensive, time-consuming, and costly, making it difficult to collect comprehensive data on crop health and issues such as insects, weeds, and diseases effectively.
Innovation Solution
A multi-legged robot, such as a quadruped robot, is designed to traverse agricultural fields along predefined trajectories, equipped with sensors to capture images and data on crop conditions, using different gaits to adapt to terrain and crop phenotypic traits, and process images using phenotypic machine learning models to infer crop health and detect issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual crop scouting is performed by growers and scientists, then comprehensive data collection on crop health is possible, but it requires intensive labor, time, and costs
Solution Approach 1:
The autonomous robot performs crop scouting independently without human intervention. It navigates fields, captures images, and analyzes crop health data automatically, eliminating the need for manual scouting while maintaining comprehensive data collection capabilities
Solution Approach 2:
The patent replaces the manual mechanical scouting process with an autonomous robotic system equipped with sensors and machine learning models. The robot uses computer vision and phenotypic analysis to detect crop issues, substituting human labor with automated technological systems
2Productivity
If the agricultural field size increases to embrace high yield crops, then production capacity improves, but manual scouting becomes overwhelming and less effective
Solution Approach 1:
The autonomous robot independently navigates and scouts large agricultural fields without requiring human operators. It autonomously plans trajectories, captures data, and analyzes crop conditions, making field scouting feasible even in extensively large areas that would be overwhelming for manual inspection
Solution Approach 2:
The patent introduces autonomous robotic technology as a new dimension in crop scouting operations. This technological dimension enables effective monitoring of large-scale fields by transitioning from manual ground-based inspection to automated robotic systems with enhanced sensing and analysis capabilities
3Measurement precision
If multiple sensors are used to capture comprehensive crop data, then measurement accuracy improves, but device complexity increases
Solution Approach 1:
The autonomous robot integrates multiple sensors (vision sensors, thermal sensors, moisture sensors) into a single multi-functional platform. This universal system performs diverse functions including image capture, temperature measurement, and soil moisture detection, achieving comprehensive crop monitoring while consolidating complexity into one integrated device
Solution Approach 2:
The patent combines multiple sensing modalities and analysis functions into a unified autonomous robot system. By merging vision systems, environmental sensors, and machine learning models into one integrated platform, the system achieves comprehensive crop health assessment while managing complexity through consolidation rather than separate systems
Data Source
AI summary
Implementations are described herein for reducing the time and costs associated with the crop scouting in a crop field. In various implementations, a method is implemented using one or more processors, and the method include: operating, based on a type and arrangement of a crop field, a robot to travel along a trajectory through the crop field using a first gait. The robot includes one or more vision sensors. The first gait includes a first repeating cycle of poses of the robot. The method can further include: synchronizing operation of one or more of the vision sensors with one or more poses of the first repeating cycle of poses of the multi-legged robot to capture one or more initial sequences of images depicting one or more points-of-interest of crops growing in the crop field.