Vision-based weed identification with farm robots

Robots in agriculture offer new opportunities for real time weed identification and quick removal operations. Weed identification and control remains one of the most challenging task in agriculture, particularly in organic agriculture practices. Considering environmental impacts and food quality, the excess use of chemicals in agriculture for controlling weeds and diseases is decreasing. The cost of herbercides and their field applications must be optimized. As an alternative, a smart weed identification technique followed by the mechanical and thermal weed control can fulfill the organic farmers’ expectations. The smart identification technique works on the concept of ‘shape matching’ and ‘active shape modeling’ of plant and weed leafs. The automated weed detection and control system consists of three major tools. Such as: i) eXcite multispectral camera, ii) LTI image processing library and iii) Hortibot robotic vehicle. The components are combined in Linux interface environment in the eXcite camera associate PC. The laboratory experiments for active shape matching have shown interesting results which will be further enhanced to develop the automated weed detection system. The Hortibot robot will be mounted with the camera unit in the front-end and the mechanical weed remover in the rear-end. The system will be upgraded for intense commercial applications in maize and other row crops

Wildlife Communication

This report contains a progress report for the ph.d. project titled “Wildlife Communication”. The project focuses on investigating how signal processing and pattern recognition can be used to improve wildlife management in agriculture. Wildlife management systems used today experience habituation from wild animals which makes them ineffective. An intelligent wildlife management system could monitor its own effectiveness and alter its scaring strategy based on this

Gå til stålet mod ukrudtet i korn

Article explaining the use of inter-row cultivation for weed control in cereal

Optimization of Inter-Row Spacing and Nitrogen Rate for the application of Vision Guided Inter-Row Weeding in Organic Spring Cereals

Flex-tine weed harrowing conducted as a full-width operation treating both crop and weeds is the principal method for direct weed control in organic spring cereals in Northern Europe. Results with this technology have varied considerably where especially crop injuries and control failures against tall-growing and tap-rooted weed species have been major drawbacks. New camera technology capable of detecting crop rows makes it possible to employ selective weed control in spring cereals. Normally cereals are grown at 12.5 cm row spacing in Northern Europe but even a moderate extension of the row spacing can make enough room for implementing automatically steered inter-row hoeing. Experiences from practice have shown that camera-based steering systems can guide a hoe blade accurately in a 20-25 cm wide inter-row space. The steering systems have also improved work rates by increasing implement width and forward speeds and the technology is gradually being employed on an increasing number of organic farms. Growers claim that crop injuries are negligible and weeding effectiveness against problematic weed species has improved compared with weed harrowing. However, the cereal cropping system has not been optimized to the usage of inter-row cultivation. Intra-row weeds, i.e. those growing in the crop lines, are not controlled and increasing the row spacing to 25 cm or more may cause a yield penalty. The aim of this study was to investigate the interaction between inter-row cultivation, inter-row spacing and nitrogen rate on weed and crop growth. Results are reported from two years field experiments with spring barley and spring wheat. It was aimed to maintain a constant seed rate for all five row spacing studied (12.5, 15, 20, 25 and 30 cm), which gave a higher crop density in the rows with increasing row spacing. A denser intra-row crop stand would improve the suppression of surviving intra-row weeds and partly compensate for the more weed growth that wider row spacing would cause by allowing more light penetration into the crop canopy. It was found that maintaining the seed rate when increasing row spacing was important for preserving crop yields. The best results in terms of weeding effectiveness and crop yield were achieved with 15 and 20 cm row spacing and high N rate; most evident in spring barley. It was seen that the traditional ‘Ducksfoot’ blade is not an optimal solution for inter-row cultivation at small row spacing. As a consequence, a new blade has been developed which is also presented at the WSSA 2016 Annual Meeting

Nyt redskab på vej til radrensning i efterafgrøder med stubrester

I forskningsprojektet RowCrop er forskere fra Aarhus Universitet sammen med virksomheden AGRONINTELLI i gang med at udvikle redskaber til et nyt dyrkningssystem med plantevækst på marken året rundt. Sådan et system har mange fordele, men stiller også nye krav til dyrkningsteknik og markredskaber, hvis det skal kunne bruges af økologer, som ikke har mulighed for at benytte sig af ukrudtssprøjtning. Plantedække året rundt kan først og fremmest forebygge udvaskning af næringsstoffer og erosion og sikre en bedre jordstruktur, men et sådant dyrkningssystem kræver, at man etablerer en efterafgrøde i forbindelse med rækkerensning i hovedafgrøden. Den metode er velkendt, men mekanisk rensning af efterafgrøden i hovedafgrødens stub er en udfordrin

FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.Comment: Submitted to special issue of MDPI Sensors: Sensors in Agricultur

A Diagnostic System for Improving Biomass Quality Based on a Sensor Network

Losses during storage of biomass are the main parameter that defines the profitability of using preserved biomass as feed for animal husbandry. In order to minimize storage losses, potential changes in specific physicochemical properties must be identified to subsequently act as indicators of silage decomposition and form the basis for preventive measures. This study presents a framework for a diagnostic system capable of detecting potential changes in specific physicochemical properties, i.e., temperature and the oxygen content, during the biomass storage process. The diagnostic system comprises a monitoring tool based on a wireless sensors network and a prediction tool based on a validated computation fluid dynamics model. It is shown that the system can provide the manager (end-user) with continuously updated information about specific biomass quality parameters. The system encompasses graphical visualization of the information to the end-user as a first step and, as a second step, the system identifies alerts depicting real differences between actual and predicted values of the monitored properties. The perspective is that this diagnostic system will provide managers with a solid basis for necessary preventive measures