IoT Based Farmland Protection System

IEEE Access, 2022

Smart agriculture techniques have recently seen widespread interest by farmers. This is driven by several factors, which include the widespread availability of economically-priced, low-powered Internet of Things (IoT) based wireless sensors to remotely monitor and report conditions of the field, climate, and crops. This enables efficient management of resources like minimizing water requirements for irrigation and minimizing the use of toxic pesticides. Furthermore, the recent boom in Artificial Intelligence can enable farmers to deploy autonomous farming machinery and make better predictions of the future based on present and past conditions to minimize crop diseases and pest infestation. Together these two enabling technologies have revolutionized conventional agriculture practices. This survey paper provides: (a) A detailed tutorial on the available advancements in the field of smart agriculture systems through IoT technologies and AI techniques; (b) A critical review of these two available technologies and challenges in their widespread deployment; and (c) An in-depth discussion about the future trends including both technological and social, when smart agriculture systems will be widely adopted by the farmers globally. INDEX TERMS Smart agriculture, Internet of Things (IoT), smart irrigation, organic farming, artificial intelligence (AI), big data.

IEEE Access

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 the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup's started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.

2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), 2023

Tiny Machine Learning (TinyML), a branch of machine learning that focuses on the effectiveness of machine learning on extremely constrained edge machines, is flourishing. Deep learning techniques are being used more frequently lately in a variety of data-intensive and time-sensitive Internet of Things (IoT) apps. Because MCUs lack resources like RAM, deploying new methods like Deep Neural Networks (DNN) models on them has proven challenging. However, recent developments in the TinyML space promise to create a completely new class of peripheral apps. By eliminating the need for the cloud's omnipresent computing support, which uses power and presents risks to data security and privacy, TinyML paves the way for the development of original apps and services. Traditional machine learning needs a lot of processing capacity to predict a scenario. This computational capacity will be moved from high-end systems to low-end devices thanks to the TinyML method for machine learning on small devices. To keep the precision of the learning models, enable resource-efficient small edge devices to manage the training and deployment process, maximize computing capacity, and enhance dependability are some of the challenges presented by this change. Here in this paper, we propose a efficient method to detect animals near farmland for security purposes using TinyML and compared with many algorithms and their effectiveness.

CENTRAL ASIAN JOURNAL OF THEORETICAL & APPLIED SCIENCES, 2022

Farming agriculture began 12000 years back. Neolithic age conceived an offspring of civilization, Farming and later being gone on as conventional farming practices. India is an agricultural country. Mostly, Indian Farming is subject to rains, soil, and environmental challenges. The rancher can screen horticultural fields with the aggregation of information from sensors, actuators, and current electronic gadgets. Shrewd Agriculture can conjecture climate information, turning ON the siphon engine and recognizing the sogginess of soil in terms of dampness levels with the assistance of sensors connected to handle module Arduino-UNO. The Agriculture stick proposed through this paper coordinates with Arduino Technology, Breadboard, and blended in with various sensors and live information feed can be gotten online through cell phone. India Monitoring environmental circumstances are the main consideration for working on productive harvests' yield. The component of this paper incorporates the advancement of a framework that can screen temperature, humidity, and moisture for the development of creatures that might obliterate the yields in rural fields through sensors utilizing the Arduino board.

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

European Journal of Information Technologies and Computer Science

Regardless of how the public perceives agriculture, the reality is that today's agriculture industry is more data-driven, precise, and sophisticated than ever before. The rapid rise of Internet-of-Things (IoT)-based technologies has transformed practically every industry, including "smart agriculture," which has moved away from statistical to quantitative techniques. Smart agriculture allows for more efficient fertilizer use and less water waste. The goal of the smart agribusiness research is to create a dynamic and emotionally supportive farm management network. IoT is being used in agriculture to learn about the agricultural field by using sensors for field monitoring and control. Sensors are used to acquire a better understanding of the crop field for monitoring, field control, and other applications. We'll look at how Internet of Things architectures aid farm fields in this research. The entire Internet of Things (IoT) Solicitations in Agriculture, as well as h...

Advances in Intelligent Systems and Computing, 2021

Agriculture is the primary source of food supply for all countries in the world. The issue of agriculture has always been hampering the growth and development of the country. To further improve this condition, we need to deploy smart and precision agriculture by revamping the old methods of agriculture. Hence, the paper aims at making agriculture smart and precise by using Internet of things (IoT) technologies. This paper proposes a system design for an IoT-based agriculture monitoring system. It also suggests a cloud-based mobile application for easy monitoring and analyzing the farms at the farmer's point of view. The proposed work deals with real-time observation by efficiently using the most reliable sensors. The main features of this works are the integration of various agriculture-related sensors such as moisture, temperature, and humidity with the Raspberry Pi and a mobile application. It also stores the recorded data on a ThinkSpeak cloud for future prediction and improving the quality of the crop. This work integrates a passive infrared sensor (PIR) to monitor the intrusion of animals or human interference. The live monitoring of the sensor's values using cloud computing and a mobile-based application was also analyzed. The proposed system prototype was successfully implemented, executed, and tested. It is a low cost, easy to use for the farmers to efficiently monitor their farms and to improve the crop quality by reducing the wastage of energy and resources.

International journal of information technology and computer science, 2024

Farmland security in Nigeria is still a major challenge and existing methods such as building brick fences around the farmland, installing electric fences, setting up deterrent plants with spikey branches or those that have displeasing scents are no longer suitable for farmland security. This paper presents an IoT based farmland intrusion detection model using sensors and computer vision techniques. Passive Infrared (PIR) sensors and camera sensors are mounted in strategic positions on the farm. The PIR sensor senses motion by the radiation of body heat and sends a message to the raspberry pi to trigger the camera to take a picture of the scene. An improved Faster Region Based Convolutional Neural Network is developed and used for object detection and One-shot learning algorithm for face recognition in the case of a person. At the end of the detection and recognition stage, details of intrusion are sent to the farm owner through text message and email notification. The raspberry pi also turns on the wade off system to divert an intruding animal away. The model achieved an improved accuracy of 92.5% compared to previous methods and effectively controlled illegal entry into a farmland.

IEEE Access, 2019

Despite the perception people may have regarding the agricultural process, the reality is that today's agriculture industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT) based technologies redesigned almost every industry including ''smart agriculture'' which moved the industry from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture methods and creating new opportunities along a range of challenges. This article highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this technology with the traditional farming practices. IoT devices and communication techniques associated with wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are listed. How this technology helping the growers throughout the crop stages, from sowing until harvesting, packing and transportation is explained. Furthermore, the use of unmanned aerial vehicles for crop surveillance and other favorable applications such as optimizing crop yield is considered in this article. State-of-the-art IoT-based architectures and platforms used in agriculture are also highlighted wherever suitable. Finally, based on this thorough review, we identify current and future trends of IoT in agriculture and highlight potential research challenges. INDEX TERMS Food quality and quantity, Internet-of-Things (IoTs), smart agriculture, advanced agriculture practices, urban farming, agriculture robots, automation, future food expectation.

IEEE/CAA Journal of Automatica Sinica, 2021

This paper presents a comprehensive review of emerging technologies for the internet of things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories: including smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the aforementioned technologies, which demonstrate their great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs.