Wireless Channel Path-Loss Modelling for Agricultural and Vegetation Environments: A Survey

This work undertakes an extensive survey of the channel modelling methods and path-loss characterization carried out in agricultural fields and vegetation environments in an attempt to study the state-of-the-art in this field, which, though vastly explored, still presents extremely diverse opportunities and challenges. The interface for communication between nodes in a typical agricultural field is the wireless channel or air interface, making it imperative to address the impairments that are exclusive to such a communication scenario by studying the characteristics of the medium. The performance of the channel is a direct indicator of the quality of communication. It is required to have a lucid understanding of the channel to ensure quality in transmission of the required information, while simultaneously ensuring maximum capacity by employing limited resources. The impairments that are the very nature of a typical wireless channel are treated in an explicit manner covering the theoretical and mathematical models, analytical aspects and empirical models. Although there are several propagation models characterized for generic indoor and outdoor environments, these cannot be applied to agricultural, vegetation, forest and foliage scenarios due to the various additional factors that are specific to these environments. Owing to the wide variety, size, properties and span of the foliage, it also becomes extremely challenging to develop a generic predictive model for all kinds of crops or vegetation. The survey is categorized into fields containing specific crops, greenhouse environment and forest/foliage scenarios and the key findings are presented

Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse

The production of tomatoes in greenhouses, in addition to its relevance in nutrition and health, is an activity of the agroindustry with high economic importance in Spain, the first exporter in Europe of this vegetable. The technological updating with precision agriculture, implemented in order to ensure adequate production, leads to a deployment planning of wireless sensors with limited coverage by the attenuation of radio waves in the presence of vegetation. The well-known propagation models FSPL (Free-Space Path Loss), two-ray, COST235,Weissberger, ITU-R (International Telecommunications Union—Radiocommunication Sector), FITU-R (Fitted ITU-R), offer values with an error percentage higher than 30% in the 2.4 GHz band in relation to those measured in field tests. As a substantial improvement, we have developed optimized propagation models, with an error estimate of less than 9% in the worst-case scenario for the later benefit of farmers, consumers and the economic chain in the production of tomatoes.This research received fund by the Ibero-American Postgraduate University Association (AUIP)

Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses

Precision agriculture and smart farming are concepts that are acquiring an important boom due to their relationship with the Internet of Things (IoT), especially in the search for new mechanisms and procedures that allow for sustainable and efficient agriculture to meet future demand from an increasing population. Both concepts require the deployment of sensor networks that monitor agricultural variables for the integration of spatial and temporal agricultural data. This paper presents a system that has been developed to measure the attenuation of radio waves in the 2.4 GHz free band (ISM- Industrial, Scientific and Medical) when propagating inside a tomato greenhouse based on the received signal strength indicator (RSSI), and a procedure for using the system to measure RSSI at different distances and heights. The system is based on Zolertia Re-Mote nodes with the Contiki operating system and a Raspberry Pi to record the data obtained. The receiver node records the RSSI at different locations in the greenhouse with the transmitter node and at different heights. In addition, a study of the radio wave attenuation was measured in a tomato greenhouse, and we publish the corresponding obtained dataset in order to share with the research community

Experimental Validation of a Best-Fit Model for Predicting Radio Wave Propagation through Vegetation

In this study, a model for predicting radio wave propagation through vegetation at 900 and 1800MHz is proposed. An integrated model comprising of ground and foliage induced effects is evaluated with respect to experimental data obtained through drive test in and around a vegetation environment, using Test Mobile System (TEMS) investigation tools. Measured path loss was compared against predictions made by four empirical vegetation models. Results indicate that the European Cooperation in Science and Technology (COST) 235 model gives the best prediction and compare favourably with measured path loss in areas where vegetation is dominant. Although, this model showed the most accurate prediction of foliage loss in the investigated area, there is a need to modify it for enhanced signal prediction. The modified model was found to predict the measured path loss with Root Mean Square Errors (RMSEs) of 6.98dB and 10.00dB at 900 and 1800MHz, respectively. Overall, findings revealed that these RMSEs are within the acceptable range of up to 15.00dB, for quality signal prediction in related environment

Vulnerability Assessment in the Smart Farming Infrastructure through Cyberattacks

The Internet of Things (IoT) has a significant impact on agriculture. So-called Smart Farming uses drones and a variety of sensors to measure climate, irrigation, soil moisture or GPS position. With this rapid influx of technology increases the threat that vulnerabilities in those technologies are being exploited for malicious intent. To show the impact of cyberattacks on agriculture, we present a simulation of several attacks on a ZigBee-based wireless sensor network. We conduct a delay attack, an interference attack and three different routing attacks (sinkhole, blackhole and selective forwarding attack). Those attacks are simulated using NETA with the OMNET++ framework. We will show that the security of WSN is influenced by factors like energy consumption or computation power, which can conflict with other interests like low per-unit costs

Low-Cost Inventions and Patents

Inventions have led to the technological advances of mankind. There are inventions of all kinds, some of which have lasted hundreds of years or even longer. Low-cost technologies are expected to be easy to build, have little or no energy consumption, and be easy to maintain and operate. The use of sustainable technologies is essential in order to move towards a greater global coverage of technology, and therefore to improve human quality of life. Low-cost products always respond to a specific need, even if no in-depth analysis of the situation or possible solutions has been carried out. It is a consensus in all industrialized countries that patents have a decisive influence on the organization of the economy, as they are a key element in promoting technological innovation. Patents must aim to promote the technological development of countries, starting from their industrial situations

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sector in terms of sustainability and resource-saving perspective and its penetration into Qatar’s economy.Open Access funding provided by the Qatar National Library. The authors are grateful to Qatar National Research Fund (QNRF) for funding and supporting the M-NEX Project (Grant No. BFSUGI01-1120-170005) in Qatar. The M-NEX is a project of the Collaborative Research Area Belmont Forum (Grant No. 11314551)

Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%