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e Schematic representation of the food supply chain from the production phase until the final consumer.
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The increasing demand for food, both in terms of quantity and quality, has raised the need for intensification and industrialisation of the agricultural sector. The "Internet of Things" (IoT) is a highly promising family of technologies which is capable of offering many solutions towards the modernisation of agriculture. Scientific groups and resea...
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... agriculture tends to be more and more industrialised. Therefore, standardisation mechanisms at each step for the product, from the grower to the consumer, have to be adopted in order to assure food safety and quality (Fig. 8). This need has led to a growing interest in food supply chain traceability systems. Internet of Things (IoT) technologies include plenty of solutions to contribute greatly to the construction, support and maintenance of such systems. In the reviewed literature, solutions focus either on the business side of Food Supply Chain (FSC) or ...
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A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with th...
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... Several studies have demonstrated the integration of nanosensors with WSNs for precision agriculture applications. For example, Cao et al. [108] developed a WSN system for real-time monitoring of soil moisture and nitrogen levels using carbon nanotube-based capacitive sensors. The nanosensors were fabricated by depositing a layer of multi-walled carbon nanotubes (MWCNTs) on a flexible polyimide substrate, followed by encapsulation with a moisture-sensitive polymer. ...
Nanotechnology has emerged as a powerful tool for advancing precision agriculture through real-time soil monitoring. By leveraging nanoscale sensors, nanoparticles, and nanomaterials, it is now possible to continuously track key soil parameters such as moisture, nutrient levels, pH, and microbial activity at an unprecedented level of spatial and temporal resolution. This real-time data enables farmers to make informed decisions regarding irrigation, fertilization, pest control, and other management practices, ultimately optimizing crop yield and quality while minimizing environmental impact and resource use. Nanomaterials like carbon nanotubes and graphene offer unique properties such as high sensitivity, fast response time, and low power consumption, making them ideal for developing wireless sensor networks that can be deployed in the field for extended periods. Nano-functionalized hydrogels and nanoclays show promise as slow-release fertilizer carriers and water retention agents. Novel nano-biosensors can detect plant pathogens, pollutants, and other stressors at extremely low concentrations, allowing for early intervention. Data collected by nanosensors can be transmitted to cloud-based platforms for advanced analytics and integration with other precision ag technologies like variable rate application, robotics, and digital twins. However, challenges remain in terms of large-scale manufacturing, standardization, cost reduction, and addressing potential ecological and health risks of nanomaterials. Ongoing research aims to develop biodegradable and biocompatible nanostructures, improve stability and durability under harsh field conditions, and establish safety and regulatory guidelines. Multidisciplinary collaboration between nanoscientists, agronomists, engineers, and data scientists will be key to realizing the full potential of nanotechnology for sustaining global food security in the face of climate change and population growth. With responsible development and application, nanotechnology-enabled real-time soil monitoring can revolutionize how we understand and manage one of our most critical natural resources.
... With IoT, internet connection plays an important role in the system. With the internet, user from thousand miles away can remotely monitor the system through sensors or even real-time display of a camera [10], [11]. The use of IoT in agriculture can bring many benefits such as providing more efficient operation, improving worker safety, equipment monitoring, improving crops and weather forecasting. ...
Fertigation system has been widely used by farmers to automate some processes of crops productions. A conventional system requires workers to prepare a fertilizer mixture, before transferring it into a main storage tank to be mixed with water. Then, electrical conductivity (EC) of the mixture will be measured. The existing fertigation system still relies heavily on workers and is manually operated and prone to human error. Therefore, internet of things (IoT) based fertigation system has been developed to deliver the fertilizer mixture with consistent EC value automatically to the plants. The main system controller is designed using ESP32 development module. The operation of the system can be monitored using an IoT dashboard and farmers can also control the system remotely. Alert will be given to the farmers if the condition of the system or plant does not meet the predefined settings. The values of EC together with temperature and humidity sensors are recorded for further analysis. A testbed is set up to provide fertigation to 120 polybags eggplants. Using the proposed fertigation system, the eggplants have been harvested earlier, therefore reducing the fertilizer usage. The cost of this IoT based fertigation system is lower compared to existing commercial products.
... Signal interference causes data loss and reduces the reliability of the systems(Elijah et al., 2018). Wireless signal propagation strength, communication range, and wireless link quality depend on the humidity, temperature, crop growth status, and crop morphological characteristics in agriculture fields(Tzounis et al., 2017) (Cama- Pinto et al., 2019. Thus, wireless signal propagation strength simulation and visualization software are essential in the future for the mass installation of Ag-IoT sensor nodes. ...
Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.
We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska has a unique agricultural industry, including 8.6 million irrigated croplands accounting for 14.8% of all irrigated croplands in the United States and a significant amount of under-connected farmlands, we decided to address these two pressing issues. Chapters three and five discuss the design and development of the AICropCAM platform. AICropCAM can extract useful information such as crop type, canopy coverage, and pest pressure and transmit it to cloud servers through low-power, longrange communication mediums using deep learning models. This platform can reduce the digital divide in crop monitoring digital solutions.
Chapter four discusses an effort to predict the soil water properties, including soil water content, soil water deficit, and remaining water content, through machine learning models that depend on the above canopy sensors and real-time weather data. The current models were trained using 2022 and 2023 data, showing promising prediction accuracy above 95%. However, these models must be tested in the long run for commercial use.
At the top level, the case studies discussed in this dissertation will help reduce the rural-urban gap and digital divide in agriculture. Additionally, the proposed AICropCAM and the above-ground sensor use for soil water property estimation can increase pivot irrigation systems' crop monitoring and improve irrigation decision-making tools. Ultimately, the case studies in this dissertation conclude that the integration of AI and IoT can advance the crop monitoring practices used for precision agriculture in midwestern irrigated cropping systems.
... These parameters include temperature, humidity, soil moisture, light intensity, and nutrient levels. The data collected by sensors enable horticulturists to make informed decisions, optimize resource use, and adapt cultivation practices to changing climate conditions (Tzounis et al., 2017). ...
This book delves into the dynamic landscape of modern horticulture, exploring a myriad of innovative techniques and technologies aimed at fostering sustainable practices and maximizing productivity. With a comprehensive array of chapters authored by leading experts in the field, this book serves as a vital resource for professionals, researchers, and students seeking to stay abreast of the latest advancements in horticultural science.
The chapters within this volume cover a diverse range of topics, each offering valuable insights into key areas of innovation. From "Climate-Smart Horticulture" to "Precision Irrigation in Greenhouse Horticulture," readers gain a deep understanding of how cutting-edge methodologies are being employed to adapt to changing environmental conditions and optimize resource usage. Additionally, chapters such as "Sustainable Horticultural Water Management" and "Optimizing Nutrient Management Strategies" provide critical perspectives on addressing challenges related to water scarcity and nutrient deficiency, crucial for ensuring long-term sustainability in horticultural production. Furthermore, this book explores the untapped potential of underexploited horticultural crops and discusses strategies for enhancing crop productivity and quality through advancements in biotechnology. It also sheds light on the importance of integrated pest management and post-harvest innovations in modern horticulture, emphasizing the need for holistic approaches to pest control and food preservation. With "Sky-High Harvests," the book takes a unique turn by examining the role of horticulture in urban landscapes, showcasing how innovative practices can contribute to sustainable abundance in cities.
... The pervasive integration of the internet of things (IoT) across various domains has facilitated the proliferation of interconnected devices, revolutionizing numerous industries, notably agriculture [1,2]. Amidst the sphere of smart agriculture, characterized by the dominance of IoT technology, a particularly notable segment pertains to livestock farming [3,4]. ...
The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model’s precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.
... This shift has given rise to the emergence of edge computing [1]. The application scope of edge computing extends widely across industry and manufacturing, agriculture, healthcare, urban management, and environmental protection [2][3][4][5], leveraging the transformative potential of the Internet of Things (IoT). ...
Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme.
... These parameters include temperature, humidity, soil moisture, light intensity, and nutrient levels. The data collected by sensors enable horticulturists to make informed decisions, optimize resource use, and adapt cultivation practices to changing climate conditions (Tzounis et al., 2017). ...
This literature explores the dynamic landscape of climate-smart agriculture, presenting a comprehensive overview of challenges and future directions in the face of climate change. Addressing the uncertainties of climate variability, resource scarcity, and biodiversity loss, the literature emphasizes strategic solutions for resilient and sustainable agricultural practices. From efficient water management to the cultivation of drought-resistant crop varieties, the integration of precision technologies, and the imperative for global collaborations, the outlined strategies provide a roadmap for adaptation in an era of unpredictable weather patterns. Furthermore, challenges related to market dynamics, policy coherence, and economic viability underscore the need for a collaborative and informed global response. Bridging the digital divide, promoting consumer awareness, and enhancing capacity building emerge as critical components of a comprehensive strategy for climate-smart agriculture. The conclusion envisions a future where science, policy, and community engagement converge to create adaptive and sustainable agricultural systems, ensuring both food security and environmental stewardship. By embracing this holistic approach, the agricultural sector can not only navigate the complexities of climate change but also thrive in the face of uncertainty, fostering a resilient and sustainable future for global food systems. This chapter serves as a valuable resource for researchers, policy-makers, and practitioners seeking a deeper understanding of the challenges and potential pathways toward climate-smart agriculture in the 21st century.
... One example of robotics in crop monitoring is the use of autonomous ground vehicles equipped with sensors and imaging technologies. These vehicles can navigate through agroforestry systems and collect data on various parameters, such as plant height, canopy cover, and soil moisture [5]. This data can be used to create detailed maps of agroforestry systems, which can help farmers to identify areas of concern and optimize management practices. ...
The integration of robotics and automation in modern agricultural practices has revolutionized the way we approach farming, particularly in the field of agroforestry. This article explores the various applications of robotics and automation in agroforestry, including precision planting, crop monitoring, pest and disease detection, and harvesting. By examining case studies and research findings, we highlight the benefits of these technologies in terms of increased efficiency, reduced labor costs, and improved crop yields. Furthermore, we discuss the challenges and limitations of implementing robotics and automation in agroforestry systems, as well as potential future developments. This article provides valuable insights for researchers, farmers, and policymakers interested in the intersection of technology and sustainable agriculture.
... These advancements have been pivotal in addressing sustainability objectives by investigating water quality and microbial life in hydroponic cultivation contexts (Paucek et al., 2023). Furthermore, the period saw the increasing application of machine learning (ML)-driven diagnostics as essential tools for preemptive pest and disease identification highlighting the role of data-driven learning in agriculture (Tzounis et al., 2017). The burgeoning world of the Internet of Things (IoT) also saw application in real-time farm monitoring and management integrating advanced technologies to enhance agricultural efficiency and productivity (Ahmed et al., 2018;Raj et al., 2021;Namana et al., 2022). ...
Vegetable cultivation stands as a pivotal element in the agricultural transformation illustrating a complex interplay between technological advancements, evolving environmental perspectives, and the growing global demand for food. This comprehensive review delves into the broad spectrum of developments in modern vegetable cultivation practices. Rooted in historical traditions, our exploration commences with conventional cultivation methods and traces the progression toward contemporary practices emphasizing the critical shifts that have refined techniques and outcomes. A significant focus is placed on the evolution of seed selection and quality assessment methods underlining the growing importance of seed treatments in enhancing both germination and plant growth. Transitioning from seeds to the soil, we investigate the transformative journey from traditional soil-based cultivation to the adoption of soilless cultures and the utilization of sustainable substrates like biochar and coir. The review also examines modern environmental controls highlighting the use of advanced greenhouse technologies and artificial intelligence in optimizing plant growth conditions. We underscore the increasing sophistication in water management strategies from advanced irrigation systems to intelligent moisture sensing. Additionally, this paper discusses the intricate aspects of precision fertilization, integrated pest management, and the expanding influence of plant growth regulators in vegetable cultivation. A special segment is dedicated to technological innovations, such as the integration of drones, robots, and state-of-the-art digital monitoring systems, in the cultivation process. While acknowledging these advancements, the review also realistically addresses the challenges and economic considerations involved in adopting cutting-edge technologies. In summary, this review not only provides a comprehensive guide to the current state of vegetable cultivation but also serves as a forward-looking reference emphasizing the critical role of continuous research and the anticipation of future developments in this field.
... These advancements have been pivotal in addressing sustainability objectives by investigating water quality and microbial life in hydroponic cultivation contexts (Paucek et al., 2023). Furthermore, the period saw the increasing application of machine learning (ML)-driven diagnostics as essential tools for preemptive pest and disease identification highlighting the role of data-driven learning in agriculture (Tzounis et al., 2017). The burgeoning world of the Internet of Things (IoT) also saw application in real-time farm monitoring and management integrating advanced technologies to enhance agricultural efficiency and productivity (Ahmed et al., 2018;Raj et al., 2021;Namana et al., 2022). ...
