Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470

Integrating Technologies for Scalable Ecology and Conservation

Integration of multiple technologies greatly increases the spatial and temporal scales over which ecological patterns and processes can be studied, and threats to protected ecosystems can be identified and mitigated. A range of technology options relevant to ecologists and conservation practitioners are described, including ways they can be linked to increase the dimensionality of data collection efforts. Remote sensing, ground-based, and data fusion technologies are broadly discussed in the context of ecological research and conservation efforts. Examples of technology integration across all of these domains are provided for large-scale protected area management and investigation of ecological dynamics. Most technologies are low-cost or open-source, and when deployed can reach economies of scale that reduce per-area costs dramatically. The large-scale, long-term data collection efforts presented here can generate new spatio-temporal understanding of threats faced by natural ecosystems and endangered species, leading to more effective conservation strategies

A comparison of ground-based methods for obtaining large-scale, high-resolution data on the spring leaf phenology of temperate tree species

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DEVELOPMENT OF A FIELD-BASED MOBILE PLATFORM FOR PLANT PHENOTYPING

Design, implementation and performance verification of an affordable field-based high-throughput plant phenotyping platform for monitoring Canola plants, including both data acquisition/visualization software and measurement system, was the main objective of this research. The primary motivation for this research is the fact that breeders need a well-organized approach and efficient tool to monitor and analyze a number of plant traits to achieve a higher yield. At the moment, manual measurement is a conventional approach to gather the required information for plant analysis. Nevertheless, manual measurement has many limitations especially to study a large-scale field. To address this bottleneck, a high-throughput plant phenotyping platform (HTPP) was developed which consists of a data acquisition system, a data storage unit, and a data visualization and analysis software. Such an HTPP will be an essential asset for breeders to conveniently gather a comprehensive database which contains various information such as a plant height, temperature, Normalized Difference Vegetation Index (NDVI), etc. To develop and implement such an HTPP, first, the overall system block diagram and required algorithms were drawn. Then to find the optimum set of equipment according to the requirement of this application, the performance of different sensors and devices were examined using literature search and experimental examinations in the laboratory setting. Then a mechanical boom was attached to the rear of a farm vehicle (a Swather) to carry different sensors, cameras and other measurement equipment (mechanical development of the boom structure was carried out by other members of the research team). A control box containing power supplies, safety fuses, and a data logger unit was attached to the farm vehicle, and a program was developed for data logger to read sensors signals as well as GPS data for data geo-referencing and future retrieval purposes. The efficiency of different system architecture including different data transmission networks was examined by conducting several field tests to minimize existing errors such as delays in synchronizing different steps. Three programs were developed in MATLAB GUI for image acquisition via webcam and DSLR cameras as well as a central program for data processing and interactive data visualization. The indoor tests were performed at the Robotics laboratory, University of Saskatchewan and outdoor experiments were performed on a Canola nursery at Cargill Canada, Aberdeen, SK, throughout spring-summer 2016 and 2017. Finally, the performance and effectiveness of the developed field-based phenotyping platform was validated by various measures such as conducting some manual measurements and comparing the results with the values given by the platform. According to the achieved results, both hardware and software components of the proposed system meet the requirements of a field-based plant phenotyping platform as an essential asset for breeders for comprehensive study of Canola plants or any other cultivars as a result of some minor design modifications. The main contributions of this study to plant phenotyping research are autonomous image acquisition capability, enhancement of the data acquisition cycle to minimize data geo-referencing error, development of a modular program for data visualization in MATLAB, and faster data collection in a high-throughput fashion (almost 125 times faster)

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application