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

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species