The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Development of canopy vigour maps using UAV for site-specific management during vineyard spraying process

Site-specific management of crops represents an important improvement in terms of efficiency and efficacy of the different labours, and its implementation has experienced a large development in the last decades, especially for field crops. The particular case of the spray application process for what are called “specialty crops” (vineyard, orchard fruits, citrus, olive trees, etc.)FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205)This research was partially funded by the “Ajuts a les activitats de demostració (operació 01.02.01 de Transferència Tecnològica del Programa de desenvolupament rural de Catalunya 2014-2020)” and an FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under the LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205).This research was partially funded by the “Ajuts a les activitats de demostració (operació 01.02.01 de Transferència Tecnològica del Programa de desenvolupament rural de Catalunya 2014-2020)” and an FI-DGR grant from Generalitat de Catalunya (2018 FI_B1 00083). Research and improvement of Dosaviña have been developed under LIFE PERFECT project: Pesticide Reduction using Friendly and Environmentally Controlled Technologies (LIFE17 ENV/ES/000205)Postprint (updated version

Machine Vision Systems – A Tool for Automatic Color Analysis in Agriculture

It was in the early 1960s when machine vision systems initiated researchers and developers have worked on building machines that perform tasks of acquisition, processing, and analysis of images in a wide range of applications for different areas. Currently, along with the new technological advances in electronics, computer systems, image processing, pattern recognition, and mechatronics, it has arose the opportunity to improve machine vision systems development with affordable implementations at lower cost. A machine vision system is the combination of several high-tech techniques, including both hardware and software, used to acquire, process, and analyze images on a machine, which contributes with a set of tools for the extraction of features, such as color and dimension parameters, texture, chemical components, disease detection, freshness, assessment, modeling, and control, among others. Based on former paragraphs, we could say that machine vision systems are appropriate to improve the actual agricultural systems making them more useful, efficient, practical, and reliable