Application of a new Internet-based decision support model for integrated weed management in winter wheat and maize (DSS-IWM) - experiences from practical applications

Unter Berücksichtigung der allgemeinen Grundsätze des Integrierten Pflanzenschutzes (Richtlinie 2009/128/EG Anhang III) wurde im Rahmen eines dreijährigen europäischen ERA-Net-Projektes (Co-ordinated Integrated Pest Management in Europe - C-IPM) ein Internet-gestütztes Entscheidungshilfemodell für die Unkrautbekämpfung in Winterweizen und Mais entwickelt (DSS-IWM). Der Prototyp dieses DSS-Modells, IPMwise, wurde Rahmen des Projektes in drei europäischen Ländern erarbeitet, geprüft und verbessert. Die Behandlungsvorschläge des Programms richten sich nach der aktuellen Verunkrautung der Fläche und beruhen auf Dosis-Wirkungsdaten und spezifischen Ziel-Wirksamkeiten. Das Programm soll sowohl Landwirte als auch Berater verlässlich dabei unterstützen, Unkräuter zum richtigen Zeitpunkt mit den geeignetsten Herbiziden in optimierter Aufwandmenge zu bekämpfen und somit dazu beitragen, den Herbizidaufwand zu reduzieren, ohne Ertragseinbußen zu riskieren. In die Entscheidungen werden lokale Bedingungen, Schadensschwellen und ökonomische Berechnungen der Behandlungen einbezogen. Das Programm soll zukünftig auch als Tablet- oder Smartphone-Version dem Anwender zur Verfügung stehen. Validierungsversuche an verschiedenen Standorten in Deutschland zeigten, dass Wirkungsgrade sowohl im Mais als auch im Winterweizen nach Behandlungsvorschlägen des DSS-Programms im Mittel etwas niedriger waren als nach der lokalen Standardbehandlung, an einzelnen Standorten aber gleich hoch. Der Behandlungsindex in wurde in den DSS-Varianten bis zu 50 % verringert, wodurch Kosteneinsparungen für Herbizide von 50 % bis 60 % möglich waren. Das Programm ist demnach geeignet, um ökologische und ökonomische Ziele der Unkrautregulierung im Rahmen der Integrierten Unkrautbekämpfung zu fördern.In accordance with the general principles of Integrated Pest Management (Directive 2009/128/EC Annex III), an Internet-based decision support model for weed control in winter wheat and maize (DSS-IWM) was developed as part of a three-year European ERA-Net project (Co-ordinated Integrated Pest Management in Europe - C-IPM). The prototype of this DSS model, IPMwise, was developed, tested and improved in three European countries. The treatment suggestions of the program are based on the current weed infestation of the field, on dose-response data and on specific target efficiencies. The program will provide reliable support to both farmers and advisors in controlling weeds at the right time with the most appropriate herbicides in optimised application rates, thus helping to reduce herbicide use without risking yield losses. Decisions are based on local conditions, damage thresholds and economic calculations of treatments. In future, the program will also be available to users as a tablet or smartphone version. Validation trials at various sites in Germany showed that the average efficacy in both maize and winter wheat according to treatment suggestions of the DSS program was slightly lower than according to the local standard treatments, but at many sites it exceeded 90%. The treatment index in the DSS variants was reduced by up to 50%, resulting in cost savings for herbicides of 50% to 60%. The program is therefore suitable for supporting the ecological and economic objectives of weed control within the framework of Integrated Weed Control

DSS-IWM: An improved European Decision Support System for Integrated Weed Management

In the frame of the European ERA-Net project “Coordinated Integrated Pest Management in Europe (C-IPM)” scientists from Germany, Denmark and Spain design and customise an innovative online decision support system for integrated weed control (DSS-IWM) in maize and winter wheat. The project runs from 2016 to 2019 with the aim to assist farmers and farm advisors in treating weeds in crops at precisely the right times and the most efficient products in the right amounts. DSS-IWM can, therefore, contribute to reducing herbicide consumption markedly without affecting the yield. It will support reliable decisions based on local conditions and will consider thresholds for weed densities, include economic calculations of treatment costs. The basis of herbicide recommendations is the database and the calculation/mathematics of the DSS-IWM, especially dose-response-relations of herbicides. If data gaps appear pot trials with respective weeds and herbicides are carried out. New features and information are continuously filled in. Additionally, in all countries field trials in maize and winter wheat are carried out to validate the DSS

Decision Support Systems for Weed Management

Editors: Guillermo R. Chantre, José L. González-Andújar.Weed management Decision Support Systems (DSS) are increasingly important computer-based tools for modern agriculture. Nowadays, extensive agriculture has become highly dependent on external inputs and both economic costs, as well the negative environmental impact of agricultural activities, demands knowledge-based technology for the optimization and protection of non-renewable resources. In this context, weed management strategies should aim to maximize economic profit by preserving and enhancing agricultural systems. Although previous contributions focusing on weed biology and weed management provide valuable insight on many aspects of weed species ecology and practical guides for weed control, no attempts have been made to highlight the forthcoming importance of DSS in weed management. This book is a first attempt to integrate 'concepts and practice' providing a novel guide to the state-of-art of DSS and the future prospects which hopefully would be of interest to higher-level students, academics and professionals in related areas

Tien havaitseminen vaikeissa sääolosuhteissa RGB- ja LiDAR-dataa hyödyntäen

Autonomous vehicles have previously used road markings as a reference for drivable area detection. For autonomous driving to be possible in weather conditions where these markings are not visible (for example snow cover, ice, heavy rain), the drivable area needs to be determined by other means. In this work the use of machine learning models based on fully convolutional neural networks is evaluated for this task. The models use data projected into bird's eye view format, enabling detections agnostic to variations in sensor properties, such as resolution. In order to find an optimal model architecture, two hyperparameter searches are performed on different resolutions and parameter ranges. A tool for partially automating the process of such dataset creation is described and implemented. The tool enables a human labeler to label a single global bird's eye view image for each driving sequence, improving the labeling efficiency by a factor of thousands compared to individually labeling the camera images. The dataset tool also demonstrates real-time production of LiDAR and RGB bird's eye view frames that are used as inputs for the neural network models. The dataset tool was able to produce a dataset of sufficient quality for the machine learning model training and evaluation. Minor defects were observed that can most likely be rectified with relatively simple modifications. The models were able to successfully predict the general shape of surrounding roads, but exhibited noise on road edges and uncertainty in areas of little input data.Autonomiset autot ovat perinteisesti havainneet ajokelpoista aluetta tiemerkintöjen avulla. Jotta autonominen ajaminen olisi mahdollista sääolosuhteissa, joissa tiemerkinnät eivät ole näkyvissä (esimerkiksi lumipeitteen, jään tai kovan sateen takia), tulee ajokelpoinen alue havaita muilla tavoin. Tässä työssä arvioidaan täyskonvoluutioneuroverkkoihin perustuvien koneoppimismallien soveltuvuutta em. tarkoitukseen. Mallit hyödyntävät lintuperspektiivin projisoitua tietoformaattia, mahdollistaen sensoriominaisuuksista, kuten resoluutiosta riippumattoman havainnoinnin. Kaksi hyperparametrihakua eri resoluutioilla ja parametriavaruuksilla suoritetaan optimaalisen mallin arkkitehtuurin löytämiseksi. Työkalu tällaisen tietojoukon luomisprosessin osittaiseksi automatisoinniksi kuvataan ja implementoidaan. Työkalun ansiosta ihmisen tarvitsee merkitä tie vain yhteen ajosekvenssiä vastaavaan, globaaliin lintuperspektiivikuvaan, parantaen ihmistyön tehokkuutta monituhatkertaisesti yksittäisten kamerakuvien merkintään verrattuna. Työkalun implementaatiossa myös demonstroidaan neuroverkkomallien syötteenä käytettävien LiDAR ja RGB-lintuperspektiivikuvien reaaliajassa tuottaminen. Työkalu kykeni tuottamaan riittävän laadukkaan tietojoukon koneoppimismallin kouluttamiseksi sekä arvioimiseksi. Pieniä vikoja oli havaittavissa, mutta ne ovat luultavasti korjattavissa verraten yksinkertaisilla muutoksilla. Mallit onnistuivat havaitsemaan ympäröivien teiden yleismuodon, mutta havainnoissa oli kohinaa erityisesti teiden reunoilla sekä epävarmuutta alueilla, joilla syötedata oli vähäistä