The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.

The importance of studying coastal areas is justified by their resources, ecosystem services, and key role played in socio-economic development. Coastal landscapes are subject to increasing demands and pressures, requiring in-depth analyses for finding appropriate tools or policies for a sustainable landscape management. The present study addresses this issue globally, based on case studies from three continents: Romania (Europe), Algeria (Africa), and Vietnam (Asia), focusing on the anthropogenic pressure resulting from land use/land cover change or urban sprawl, taking into account the role of socioeconomic and political factors. The methodology consisted of producing maps and computing and analyzing indicators, correlating geospatial and socio-economic data in a synergistic manner to explore the changes of landscapes, and identify the specific driving forces. The findings show that the pressure of urbanization and tourism on coastal areas increased, while the drivers and impacts vary. Urbanization is due to derogatory planning in Romania and Algeria, and different national and local goals in Vietnam. The two drivers determine local exemptions from the national regulations, made for profit. In addition to the need for developing and enforcing policies for stopping the degradation and restoring the ecosystems, the findings underline the importance of international cooperation in policy development.

The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost.

Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine’s (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information.

The mapping of hydrothermal alteration zones associated with mineralization is of paramount importance in searching for metal deposits. For this purpose, targeting alteration zones by analyzing airborne geophysical and satellite imagery provides accurate and reliable results. In the Kelâat M’Gouna inlier, located in the Saghro Massif of the Moroccan Anti Atlas, natural gamma-ray spectrometry and ASTER satellite data were used to map hydrothermal alteration zones. Natural gamma-ray spectrometry data were processed to produce maps of Potassium (K in %), Uranium (eU in ppm), Thorium (eTh in ppm) and ratios of K/eTh and K/eU. In addition, four-band ratios were computed, on ASTER data, to map the distribution of clay minerals, phyllitic minerals, propylitic minerals, and iron oxides. The combined results obtained from geophysical and satellite data were further exploited by fuzzy logic modelling using the Geographic Information System (GIS) to generate a mineral prospectivity map. Seven hydrothermal alteration zones likely to be favorable for mineralization have been identified. They show a spatial correlation with (i) known surface prospects and mineral occurrences, (ii) the granite-encasing contact zone, and (iii) the fault zones (Sidi Flah and Tagmout faults). This research therefore provides important information on the prospecting of mineral potential in the study area.

Within an E.U.-funded project, BESS (Pocket Beach Management and Remote Surveillance System), the notion of a geographic information system is an indispensable tool for managing the dynamics of georeferenced data and information for any form of territorial planning. This notion was further explored with the creation of a WebGIS portal that will allow local and regional stakeholders/authorities obtain an easy remote access tool to monitor the status of pocket beaches (PB) in the Maltese Archipelago and Sicily. In this paper, we provide a methodological approach for the implementation of a WebGIS necessary for very detailed dynamic mapping and visualization of geospatial coastal data; the description of the dataset necessary for the monitoring of coastal areas, especially the PBs; and a demonstration of a case study for the PBs of Sicily and Malta by using the methodology and the dataset used during the BESS project. Detailed steps involved in the creation of the WebGIS are presented. These include data preparation, data storage, and data publication and transformation into geo-services. With the help of different Open Geospatial Consortium protocols, the WebGIS displays different layers of information for 134 PBs including orthophotos, sedimentological/geomorphological beach characteristics, shoreline evolution, geometric and morphological parameters, shallow water bathymetry, and photographs of pocket beaches. The WebGIS allows not only for identifying, evaluating, and directing potential solutions to present and arising issues, but also enables public access and involvement. It reflects a platform for future local and regional coastal zone monitoring and management, by promoting public/private involvement in addressing coastal issues and providing local public administrations with an improved technology to monitor coastal changes and help better plan suitable interventions.