Robotic in-row weed control in vegetables

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 eld. 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 aecting the crop. This Dropon-Demand (DoD) method allow the use of non-selective herbicides and signicant 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, ecient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benet of reduced weight, complexity and cost. The DoD system for herbicide application has been developed within and in connection with this project. The inuence 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 eect unless mixed with suitable additives. The trials document the DoD eect on weed species not previously described in literature, and with an alternative herbicide to glyphosate, iodosulfuron. A eld trial with the robot was performed in a carrot eld, and all the weeds were eectively controlled with the DoD system.iii Abstract 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. My advisor, Prof. Jan Tommy Gravdahl has the ability to delve deeply into a wide array of technical elds. He has been a solid and consistent support, which has been vital for me to bridge the divide between the industrial and academic worlds. I will strive for the opportunities to work more together.Friendships have grown both at NTNU, UMN and Adigo which will outlast this thesis. I thank you, my family, colleagues and friends for the support to complete this journey. And not the least, I thank my wife Camilla. This introduction will describe the background for the project and its motivation, and the context and relationship between each paper is described in Section 1.4. The conclusion in Chapter 3 will tie together our ndings and describe our perspective on the path forwards for Drop on De...

Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology

“…Robots are coming to agriculture in several application areas, including spraying grape vines, in-row weeding, navigation of orchards, and harvesting of fruit and vegetables. [149][150][151][152] Innovation in machine vision, coupled with GPS and RS technology, is creating the possibility for autonomous machines that can be localized at the centimeter level, provide precise identification of crop needs, and can be integrated into robust decision-making systems to provide timely crop intervention. 153 Advanced robotics could be deployed over a broad range of operational scales and types, ranging from large field operations to manipulating plants in greenhouses.…”

The Role of Advanced Manufacturing Technologies in Production Process Performance: A Causal Model

“…A similar hypothesis was made by Brecher and Lohse [49], who pointed out that computerized systems allow better control of activities. In addition, robots have been performing the heaviest and most dangerous tasks in production systems for a long time, which has also been reflected in productivity indexes [50]. Finally, Zhang, et al [51] claimed that systems based on CNC, CAM, and AMHS make production more flexible and agile.…”

High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding

“…These approaches can be implemented using relatively simple detection techniques, estimating overall plant coverage [10,13] or crop position [12,14]. Individual weed targeting for mechanical removal [15] or herbicidal micro-dosing [16,17] has also been investigated in recent years. Non-selective herbicides less effected by herbicide resistance can be utilised for micro-dosing systems, which can reduce herbicide requirements by up to 99.3% according to [18].…”