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A wireless sensor network (WSN) is a system designed to remotely monitor and control a specific phenomenon or event. This new 2-page fact sheet discusses the advantages that the WSN has over traditional stand-alone sensors and controllers, power consumption and conservation, and WSN technologies. Written by Clyde Fraisse, Janise McNair, and Thiago Borba Onofre, and published by the UF/IFAS Department of Agricultural and Biological Engineering, January 2018.  http://edis.ifas.ufl.edu/ae521

HighlightsFungal diseases are considered a major challenge for strawberry farmers.Disease risk monitoring systems can be improved with increased spatial coverage of environmental conditions.Wireless Sensor Networks (WSN) with customized nodes can efficiently collect site-specific data inputs for disease risk models.WSN providing disease risk levels with increased spatial and temporal resolution opens the opportunity for site-specific control actions.Abstract. The United States is the world’s largest producer of strawberries, harvesting over 680 million metric tons in 2017, valued at approximately $3.2 billion. Fungal diseases are considered a major challenge for strawberry farmers. Even in well-managed fields, losses from fungal diseases can exceed 50% when environmental conditions favor disease development. Anthracnose fruit rot (AFR), caused by Colletotrichum acutatum and Botrytis fruit rot (BFR), caused by Botrytis cinerea, are the most significant diseases monitored (or present) during strawberry production in Central Florida and worldwide. The Strawberry Advisory System (SAS) was developed by researchers at the University of Florida to alert strawberry growers of infection risk of AFR and BFR. SAS also recommends control actions when necessary. In Florida, the SAS uses leaf wetness duration and temperature observed at Florida Automated Weather Network (FAWN) stations and private weather stations to estimate strawberry disease risk. A good representation of local conditions is crucial, but unfortunately uncommon in modern fungal disease warning systems (FDWS), such as the SAS. In this study, we developed and deployed an in-field Wireless Sensor Network (WSN) with customized nodes (WetBerry). WetBerry is a distributed mesh network of wireless mini weather stations equipped with leaf wetness, temperature, and relative humidity sensors developed to monitor in-field environmental conditions related to the risk of fungal diseases in strawberry production. The WSN mini weather stations were installed and tested in a strawberry field during the 2017/2018 strawberry season, and the results were validated against a standard agrometeorological weather station. The WSN provided high spatial and temporal density of weather data for agricultural applications, thereby has the potential to improve the capabilities of site-specific fungal disease warning tools and control actions. Keywords: Decision making, Fungal disease, Leaf wetness, Precision agriculture, Site-specific, WSN.

Coffee has a significant economic, social, and cultural impact on Brazilian society, generating jobs for thousands of Brazilians. Good management practices such as weed control have direct and indirect benefits on coffee yield and quality. Currently, there is an increase in the infestation rate of Digitaria insularis in coffee plantations due to chemical resistance to glyphosate. In the literature, the study of the combination of glyphosate with different herbicides has been investigated, aiming at improving the efficiency of the control of Digitaria insularis. The objective of this work was to evaluate the efficiency of control of the Digitaria insularis in a coffee farm using a combination of glyphosate with clethodim and phenoxaprope-p-ethyl. Trials were conducted in a commercial coffee farm in southern Minas Gerais in a field with a two-year-old cultivar IAC Catuaí 144. The experiment was conducted under a completely randomized design with five treatments and four replications, totaling 20 experimental plots. Thirty and ninety days after application, the plant numbers were counted and the visual analysis with the use of drones and the digital camera. The results show that the use of glyphosate alone was not efficient in the power of Digitaria insularis. The results show that the herbicide mixtures were efficient in the control.