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 This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions.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

This comprehensive review paper explores the profound impact of deep learning in the context of agriculture, with a specific focus on its crucial role in crop disease analysis and management. Deep learning techniques have exhibited remarkable potential to revolutionize agricultural practices, enhancing efficiency, sustainability, and resilience. The introductory section sets the stage by emphasizing the significant role of deep learning in agriculture, offering insights into its transformative applications, including disease detection, yield prediction, precision agriculture, and resource optimization. Subsequent sections delve into the fundamental aspects of deep learning, beginning with an exploration of its relevance and its practical implementations in crop disease detection. These discussions illuminate the essential techniques and methodologies that drive this technology, stressing the critical importance of data quality, model generalization, computational resources, and cost considerations. The paper also addresses ethical and environmental concerns, emphasizing the imperative of responsible and sustainable deep learning applications in agriculture. Furthermore, the document outlines the limitations and challenges faced in this field, encompassing data availability, ethical considerations, and computational resource accessibility, offering valuable insights for future research and development. This paper underscores the immense potential of deep learning to revolutionize agriculture by improving disease management, resource allocation, and overall sustainability. While persistent challenges exist, such as data quality and accessibility, the promise of harnessing deep learning to address global food security challenges is exceptionally encouraging. This comprehensive review serves as a foundational resource for ongoing research and innovation within the agricultural domain

COST 733 - WG4: Applications of weather type classification

The main objective of the COST Action 733 is to achieve a general numerical method for assessing, comparing and classifying typical weather situations in the European regions. To accomplish this goal, different workgroups are established, each with their specific aims: WG1: Existing methods and applications (finished); WG2: Implementation and development of weather types classification methods; WG3: Comparison of selected weather types classifications; WG4: Testing methods for various applications. The main task of Workgroup 4 (WG4) in COST 733 implies the testing of the selected weather type methods for various classifications. In more detail, WG4 focuses on the following topics:• Selection of dedicated applications (using results from WG1), • Performance of the selected applications using available weather types provided by WG2, • Intercomparison of the application results as a results of different methods • Final assessment of the results and uncertainties, • Presentation and release of results to the other WGs and external interested • Recommend specifications for a new (common) method WG2 Introduction In order to address these specific aims, various applications are selected and WG4 is divided in subgroups accordingly: 1.Air quality 2. Hydrology (& Climatological mapping) 3. Forest fires 4. Climate change and variability 5. Risks and hazards Simultaneously, the special attention is paid to the several wide topics concerning some other COST Actions such as: phenology (COST725), biometeorology (COST730), agriculture (COST 734) and mesoscale modelling and air pollution (COST728). Sub-groups are established to find advantages and disadvantages of different classification methods for different applications. Focus is given to data requirements, spatial and temporal scale, domain area, specifi

SWAMP:Smart Water Management Platform Overview and Security Challenges

The intensive use of technology in precision irrigation for agriculture is getting momentum in order to optimize the use of water, reduce the energy consumption and improve the quality of crops. Internet of Things (IoT) and other technologies are the natural choices for smart water management applications, and the SWAMP project is expected to prove the appropriateness of IoT in real settings with the deployment of on-site pilots. At the same time, the more intense the use of technology is, agriculture turns new security risks, which may affect both crop development and the commodities market. A security breach may irreversibly compromise a crop and data eavesdropping may compromise price and contracts exposing sensitive data such crop quality, development or management. This paper discusses security challenges and technologies for the application of IoT in agriculture and indicates that one of the most relevant challenges to be handled in SWAMP project is dealing with the multitude of behaviors from IoT application and what would be considered as normal and what would be considered as a threat

AGRICULTURE AND CITIZEN COMPLAINTS

The paper addresses the relationship between agricultural spills and environmental complaints filed by citizens against agriculture. It also determines the influence of other factors on the likelihood of both farm spills and complaints within a region. The relationships have been estimated using a unique data set containing the number of spills and complaints along with regional data such as the stringency of environmental regulations and socio-economic variables. Different environmental regulations do appear to have an effect on the spills and complaints. By-laws on the size of manure storage facility in relation to the number of livestock housed influence the likelihood of spills within a region. Larger storages decrease the number of annual manure applications and thus the opportunity for runoff. While the required distance between a new barn and a waterway appears to have no effect on the likelihood of spills, it does decrease the probability of complaints being lodged against agriculture. Increases in the percentage of the regions zoned as agriculture also decreases the likelihood of complaining. Together the results suggest that distance between livestock producers and both environmentally sensitive areas and people are an effective means to reduce conflicts between farmers and the local community. Another policy question raised in the study was the effectiveness of using citizen complaints as an information tool in addressing environmental quality issues surrounding agriculture. There is a positive, albeit weak, positive influence between spills in a region and the number of complaints. Complaints could be used to indicate problem areas but the information signal will be noisy. Regulators will have to be aware that such complaints are more likely to come from wealthy areas when deciding upon how to react to complaints.Environmental Economics and Policy,

Sample design for quality monitoring and measurement error evaluation of large-scale longitudinal surveys

The design of samples to monitor the quality of the data being collected (data quality monitoring) and to evaluate properties of measurement errors for the survey (measurement error evaluation) in the context of large-scale longitudinal surveys is discussed. Longitudinal surveys provide historical information that allows for more complex sample designs than simple ad hoc approaches used in one-time surveys. The properties and feasibility of several classes of probability sample designs for data quality monitoring is investigated. The problem of assessing the properties of measurement error is addressed with emphasis on designing a subsample to estimate the measurement error contribution to the variance of a sample estimator. The investigation is motivated by potential applications to the United States Department of Agriculture\u27s National Resources Inventory

Power and Energy Optimized Approach towards Sustainable Mobile Ad-hoc Networks and IoT

Investigating how real-time applications in sectors like healthcare, agriculture, construction, and manufacturing can enhance their effectiveness and sustainability through the use of autonomous sensor technologies, green computing, and big data analytics is part of the work with sustainable approaches for optimising performance of networks. This authoritative guide exposes the drawbacks of conventional technology and provides techniques and tactics for addressing Quality of Service (QOS) issues and enhancing network performance. It brings together a broad team of subject-matter specialists. Several in-depth chapters cover topics like blockchain-assisted secure data sharing, intelligent management of ad hoc networks, smart 5G Internet of Things scenarios, and the use of artificial intelligence (AI), machine learning (ML), and learning techniques (DL) techniques in smart healthcare, smart factory, and smart agriculture

Routing protocols and quality of services for security based applications using wireless video sensor networks

Wireless video sensor networks have been a hot topic in recent years; the monitoring capability is the central feature of the services offered by a wireless video sensor network can be classified into three major categories: monitoring, alerting, and information on-demand. These features have been applied to a large number of applications related to the environment (agriculture, water, forest and fire detection), military, buildings, health (elderly people and home monitoring), disaster relief, area and industrial monitoring. Security applications oriented toward critical infrastructures and disaster relief are very important applications that many countries have identified as critical in the near future. This paper aims to design a cross layer based protocol to provide the required quality of services for security related applications using wireless video sensor networks. Energy saving, delay and reliability for the delivered data are crucial in the proposed application. Simulation results show that the proposed cross layer based protocol offers a good performance in term of providing the required quality of services for the proposed application

Second Eastern Regional Remote Sensing Applications Conference

Participants from state and local governments share experiences in remote sensing applications with one another and with users in the Federal government, universities, and the private sector during technical sessions and forums covering agriculture and forestry; land cover analysis and planning; surface mining and energy; data processing; water quality and the coastal zone; geographic information systems; and user development programs