Opportunities for improving irrigation efficiency with quantitative models, soil water sensors and wireless technology

Increasingly serious shortages of water make it imperative to improve the efficiency of irrigation in agriculture, horticulture and in the maintenance of urban landscapes. The main aim of the current review is to identify ways of meeting this objective. After reviewing current irrigation practices, discussion is centred on the sensitivity of crops to water deficit, the finding that growth of many crops is unaffected by considerable lowering of soil water content and, on this basis, the creation of improved means of irrigation scheduling. Subsequently, attention is focused on irrigation problems associated with spatial variability in soil water and the often slow infiltration of water into soil, especially the subsoil. As monitoring of soil water is important for estimating irrigation requirements, the attributes of the two main types of soil water sensors and their most appropriate uses are described. Attention is also drawn to the contribution of wireless technology to the transmission of sensor outputs. Rapid progress is being made in transmitting sensor data, obtained from different depths down the soil profile across irrigated areas, to a PC that processes the data and on this basis automatically commands irrigation equipment to deliver amounts of water, according to need, across the field. To help interpret sensor outputs, and for many other reasons, principles of water processes in the soil–plant system are incorporated into simulation models that are calibrated and tested in field experiments. Finally, it is emphasized that the relative importance of the factors discussed in this review to any particular situation varies enormously

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 This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions.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

Crop Management with the IoT: an Interdisciplinary Survey

In this study we analyze how crop management is going to benefit from the Internet of Things providing an overview of its architecture and components from an agronomic and a technological perspective. The present analysis highlights that IoT is a mature enabling technology, with articulated hardware and software components. Cheap networked devices may sense crop fields at a finer grain, to give timeliness warnings on stress conditions and the presence of disease to a wider range of farmers. Cloud computing allows to reliably store and access heterogeneous data, developing and deploy farm services. From this study emerges that IoT is also going to increase attention to sensor quality and placement protocol, while Machine Learning should be oriented to produce understandable knowledge, which is also useful to enhance Cropping System Simulation systems

Current status of and future opportunities for digital agriculture in Australia

In Australia, digital agriculture is considered immature and its adoption ad hoc, despite a relatively advanced technology innovation sector. In this review, we focus on the technical, governance and social factors of digital adoption that have created a disconnect between technology development and the end user community (farmers and their advisors). Using examples that reflect both successes and barriers in Australian agriculture, we first explore the current enabling technologies and processes, and then we highlight some of the key socio-technical factors that explain why digital agriculture is immature and ad hoc. Pronounced issues include fragmentation of the innovation system (and digital tools), and a lack of enabling legislation and policy to support technology deployment. To overcome such issues and increase adoption, clear value propositions for change are necessary. These value propositions are influenced by the perceptions and aspirations of individuals, the delivery of digitally-enabled processes and the supporting legislative, policy and educational structures, better use/conversion of data generated through technology applications to knowledge for supporting decision making, and the suitability of the technology. Agronomists and early adopter farmers will play a significant role in closing the technology-end user gap, and will need support and training from technology service providers, government bodies and peer-networks. Ultimately, practice change will only be achieved through mutual understanding, ownership and trust. This will occur when farmers and their advisors are an integral part of the entire digital innovation system

Aggregate Farming in the Cloud: The AFarCloud ECSEL project

Farming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate monitoring of soil and crop properties and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate Farming in the Cloud) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labor costs. Moreover, such a platform can be integrated with farm management software to support monitoring and decision-making solutions based on big data and real-time data mining techniques.publishedVersio

Wireless Sensor Networks (WSN): An Overview

The plethora of research and development efforts on Wireless Sensor Networks is an indication that the technology has emerged an active research area in recent times. In this paper, a review of this intelligent technology is undertaken. Its working mechanisms, merits, challenges, transmission technologies, simulating tools and applications are considered. The paper concludes with a clear conviction that a sound knowledge of the basics of this technology is a sine qua non to research and development of the technology

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Agricultural Production System Based On IOT

Internet of things (IoT) is not a single word, but it has gathered billions of devices in the same lane. The Internet of things has given the lives of things. Machines have a sense now like a human. It works remotely as the program has been settled inside the chip. The system has become so smart and reliable. The Internet of things has brought out changes in most of the sectors of humankind. Meanwhile, agriculture is the main strength of a country. The more the production of agricultural products increased, the world will be more completeness from food shortage. The production of agriculture can be increased when the IoT system can be entirely implemented in the agricultural sector. Most of the approaches for IoT based agriculture have been reviewed in this paper. Related to IoT based agriculture, most of the architecture and methodology have been interpreted and have been critically analyzed based on previous related work of the researchers. This paper will be able to provide a complete idea with the architecture and methodology in the field of IoT based agriculture. Moreover, the challenges for agricultural IoT are discussed with the methods provided by the researche

Agricultural Production System Based On IOT

Internet of things (IoT) is not a single word, but it has gathered billions of devices in the same lane. The Internet of things has given the lives of things. Machines have a sense now like a human. It works remotely as the program has been settled inside the chip. The system has become so smart and reliable. The Internet of things has brought out changes in most of the sectors of humankind. Meanwhile, agriculture is the main strength of a country. The more the production of agricultural products increased, the world will be more completeness from food shortage. The production of agriculture can be increased when the IoT system can be entirely implemented in the agricultural sector. Most of the approaches for IoT based agriculture have been reviewed in this paper. Related to IoT based agriculture, most of the architecture and methodology have been interpreted and have been critically analyzed based on previous related work of the researchers. This paper will be able to provide a complete idea with the architecture and methodology in the field of IoT based agriculture. Moreover, the challenges for agricultural IoT are discussed with the methods provided by the researche