The Accuracy of Farm Machinery for Precision Agriculture: A Case for Fertilizer Application

Work quality, capacity and reliability are important criteria for design and evaluation of farm equipment. With the introduction of precision agriculture, the ability to adapt to spatially variable soil and crop conditions, becomes an additional requirement. A calculation method developed by Goense (1996) to express the precision of site specific fertilizer application as the variance between the required rate, RR, and the applied rate, AR, is extended to study the effects on yield and environmental aspects. This method aims for a single characteristic of the field to evaluate performance of different techniques. The variogram of the thickness of the most characteristic functional soil layer is used in this study. With the theory of geostatistics the variance of the estimation of this thickness can be calculated as influenced by the spreading patterns of fertilizer applicators, positioning accuracy and resolution in which field information is made available. This is done for fields with different levels of variability. The WAVE simulation model for crop growth, water use and mineral behavior is used to determine the effects of application inaccuracies. The model calculations showed that a rigid scheme of one application before planting has small potential saving by site specific techniques. The working width of independently controlled sections of the spreaders have a large influence on accuracy of site specific application, though there is a clear interaction with the resolution of the required application rates for the smaller working Areas. A resolution of a 12 m grid is required to realise about one-half of the potential saving by site specific fertilizer application. The study shows the dependency of machine design on the resolution of field information

Radio wave propagation in potato fields

Abstract — Reliable communication is crucial for successful deployment of wireless sensor networks. Therefore, it is important to understand the impact of environmental conditions on the performance of the radios (Chipcon CC1000 transceivers) used in typical sensor nodes. This paper reports on an extensive set of measurements taken in a potato field, where the foliage has an important effect on the propagation of radio waves. The influence of the growth stage of the potato crop is significant. We observed a reduction of 15 dB in signal strength at 15 m between nodes, when a flowering crop is compared to a crop on its return. This effectively reduces the radio range from 23 m to 10 m. Another important result is that radio waves propagate better in conditions with a high humidity (i.e., at night and during rain). We attribute this to changes in the reflection coefficient of the top of the potato canopy. I

A Future Internet Collaboration Platform for Safe and Healthy Food from Farm to Fork

It is expected that the world population will further expand from the current 6 billion to 9-11 billion people in 2050. This faces us with an enormous challenge to feed this population and still keep production within the limits of planet Earth's carrying capacity. Smart Farming - i.e. the use of smart, data-rich ICT-services and applications, in combination with advanced hardware (in tractors, greenhouses, etc.) - can provide the much needed breakthroughs to producing enough good quality food in a safe and environmental-sound way. This paper introduces a Future Internet platform - called FIspace - for business to business collaboration that is currently being developed within Europe's Future Internet Public-Private Partnership programme (FI-PPP). On top of that a specific implementation will be made for the area of Smart Farming that will enable a global approach for Apps and Services development. It is expected that this will overcome many of the current bottlenecks in ICT development for Smart Farming such as interoperability and handling large amounts of data, and that it will lead to more agile and affordable software solutions. In this way, this development can contribute to the global challenge of producing enough safe and healthy food for the future within planet Earth's carrying capacity. New projects are planned and collaboration in Europe and beyond should further leverage the platform. The ambition is to become world-leading in this area

Analysis of metadata standards for the exchange of image datasets and algorithms in the agricultural domain : A metadata-oriented approach to identify minimum interoperability mechanisms for image data and deep learning algorithms that is used for vision-based applications in agriculture. Sprint Robotics Project PL4.0 WP7

This report discusses the importance of precision agriculture in achieving sustainability goals and the need for a basis that considers different perspectives of a data space such as interoperability, scalability, security, transparency, and data ownership. The Towards Precision Agriculture 4.0 project aims to address these perspectives to provide better-informed management decisions for farmers and the ecosystem. The current study focuses on determining minimum interoperability mechanisms concerning the standardization of image data and deep learning algorithms for vision-based applications in weed management by robots. The study adopts a metadata-oriented approach to make data and algorithms semantically interoperable and reuses existing knowledge from the Reference Model Agro (rmAgro). The results indicate the need for a balance between established standardization and agile standardization for supporting semantic interoperability, and the interoperability of preferred standards like Robot Operating System (ROS) and Open Neural Network Exchange (ONNX) is insufficient. The study results are useful for professionals and academia who work in the design and development of software for the farming business

A Farm Information Model for Development and Configuration of Interoperable ICT Components to support Collaborative Business Processes – a case of late blight protection

Farm enterprises2 The objective of this paper is to describe a farm information model and a proof of concept that demonstrates how a collaborative Business Process for farming can be configured using this farm information model. Knowledge to develop this model and a proof of concept is obtained by case study research focusing on the collaborative Business Processes of spraying and crop protection of potatoes against late blight disease. need to collaborate with numerous actors that are part of Agri-Food Supply Chain Networks (AFSCNs) such as governments, advisory services, contractors, processors, input providers and certification bodies. This collaboration is required to produce food in a more sustainable, safe and transparent manner. To collaborate efficiently and effectively, information needs to be shared within collaborative Business Processes. The information sharing within such collaborative Business Processes should be supported by an ICT infrastructure consisting of interoperable ICT Components. Currently, most of the available ICT Components are not interoperable, hindering data exchange between ICT Components of various vendors. Consequently, this situation is hindering optimization of farm production processes and collaboration in AFSCNs. Therefore, a platform, called FIspace, is being established for multiple domains that support the development and configuration of interoperable ICT Components into a system that is able to support collaborative farm Business Processes. To develop interoperable ICT Components and configure these in an easy and flexible manner to support farm enterprises, a farm information model is, amongst other models, required.The presented farm information reference model is able to describe the relations between a farm enterprise and its collaborators, the Business Processes related to the supporting ICT Components and the data messages for data exchange between ICT Components