Asterix: Robotic weed control in row crops

Abstract

Vegetables and other row-crops represent a large share of the agricultural production. There is a large variation in crop species, and a limited availability in specialized herbicides. The robot presented here utilizes the systematic cultivation techniques of row crops to navigate and operate in the field. By the use of machine vision it separates seeded vegetable crops from weed, and treat each weed within the row with individual herbicide droplets, without affecting the crop. This Dropon- Demand (DoD) method allow the use of non-selective herbicides and significant reductions in herbicide use. This thesis presents six research papers concerning the development of the DoD system and the mobile robot. The robot is tailored to its purpose with cost, maintainability, effcient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benefit of reduced weight, complexity and cost. Topics within localization and navigation for agricultural robotics have been explored. Quaternion estimation by an Extended Kalman Filter and a Non-linear complimentary filter has been implemented on an ARM Cortex M3 microcontroller. A Bayesian framework for fusing delayed Visual Odometry measurements has been explored in simulations. A Non-linear Model Predictive Controller (NMPC) has been developed and explored in simulation to enable a controller guaranteed to not sway its wheels into the crop row and subsequently damage the crop. The framework is also suitable for implementing other constrains for operation in other environments, such as greenhouses or confined spaces. Path following by an adaptive controller and a Model Reference Adaptive Controller (MRAC) has been implemented and compared in indoor trials. The DoD system for herbicide application has been developed within and in connection with this project. The influence of liquid properties viscosity and surface tension on the formation and stability of droplets has been tested in lab trials. A control circuit for synchronized control of solenoid valves was developed and tested. Indoor pot trials with four weed species demonstrated that the Drop-on-Demand system (DoD) could control the weeds with as little as 7.6 µg glyphosate or 0.15 µg iodosulfuron per plant. The results also highlight the importance of liquid characteristics for leaf retention, as the common herbicide glyphosate had no effect unless mixed with suitable additives. The trials document the DoD effect on weed species not previously described in literature, and with an alternative herbicide to glyphosate, iodosulfuron. A field trial with the robot was performed in a carrot field, and all the weeds were effectively controlled with the DoD system. The robot and DoD system represent an important contribution to the range of systems presented witin Precision Agriculture for in-row weed control - a movement which as a whole represent a paradigm shift to the environmental impact and health risks of weed control, while providing valuable new tools to the producers.