Farm Monitoring System Research Articles

Farm Monitoring System with Drones and Optical Camera Communication.

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30% compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On-Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under 10-3 in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture.

Field trial and performance evaluation of IoT poultry farm monitoring system

IoT technology revolutionizes poultry farming by enabling real-time data collection and analysis. Traditional manual methods for monitoring temperature, humidity, and AC voltage are being replaced with automated systems. The IoT setup includes three sensor nodes, CCTV, an IoT gateway, and a web server. Temperature ranges from 27 to 35°C in offfattening periods and consistently above 30°C during fattening. Humidity fluctuates between 60% to 90% in both periods. The CPU temperature remains within safe limits. Uplink data rates exceed 2 Mbps, while AC voltage initially falls below standards but improves over time.

Precision Mushroom Farm Monitoring System With Classification of Toxic Mushrooms in Punjab : A Comprehensive Review

Precision Mushroom Farm Monitoring System With Classification of Toxic Mushrooms in Punjab : A Comprehensive Review

Sustainable Agriculture Leveraging Artificial Intelligence Systems in Kenya's Agri-food Supply Chain

The Agro-food supply chain is crucial for achieving Sustainable Development Goal No. 2 of zero hunger and sustainable agriculture. However, Kenya faces significant post-harvest losses, mainly attributed to challenges in first and last-mile logistics. In the era of technological advancements, this research paper explores the potential of Artificial Intelligence (AI) to enhance the Kenyan agri-food supply chain. Building on existing information, the study focuses on AI's role in monitoring and controlling farmland outputs, optimizing supply chain logistics, and addressing fraud and counterfeiting. The research objectives include assessing AI's utility in monitoring and controlling outputs in farmlands improving supply chain efficiency, and combating fraud in agricultural inputs. Research methods involve a comprehensive literature review, analyzing case studies such as Project FARM and FAO's Fall Armyworm Monitoring and Early Warning System, and reviewing scholarly articles on AI applications in agriculture. The research results highlight the benefits of leveraging AI in farmland monitoring, climate change adaptation, supply chain logistics, and fraud prevention. AI technologies can enhance agricultural productivity, reduce transportation costs, and eliminate corruption in the supply chain. The findings suggest that integrating AI systems into the agri-food supply chain is vital for achieving sustainable agriculture in Kenya. The study concludes that AI offers innovative solutions to address the challenges faced by smallholder farmers, enhance supply chain efficiency, and contribute to achieving zero hunger and sustainable agricultural practices.

Integrating random forest model and internet of things-based sensor for smart poultry farm monitoring system

<span>The global poultry industry has encountered growing concerns related to foodborne illnesses, misuse of antibiotics, and environmental impacts. To tackle these issues, this study aims to develop an intelligent poultry farm with real-time environmental monitoring and predictive models. The primary objective is to combine a machine learning-based prediction model with internet of things (IoT) devices to gather and analyze environmental data, such as temperature, humidity, and ammonia levels, to forecast the conditions within poultry houses. These sensor data and additional information, such as feed consumption, water consumption, poultry weight, capacity, and poultry house dimensions will serve as inputs for supervised machine learning models. Among these models, the proposed random forest (RF) model, when augmented with timestamp features, achieves the highest accuracy rate of 96.665%, surpassing other models such as logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), support vector machine (SVM), and multi-layer perceptron (MLP) in identifying poultry house conditions. Additionally, this study demonstrates how the trained model can be effectively applied in a web-based monitoring system, delivering real-time data to farmers for well-informed decision-making and ultimately enhancing productivity in smart poultry farming.</span>

CROP GROWTH MONITORING SYSTEM BASED ON DRONE AND LOW POWER WAN

Smart farming is currently seen as an effective way to increase farm productivity and has gained a lot of attention from service providers. It offers a variety of applications, from identifying pests to monitoring assets. The emergence of digital technologies such as IoT and LPWANs has improved the smart farming industry, but more efficient solutions are still needed. Unmanned aerial vehicles, or drones, are increasingly being used across various civil domains, and this paper aims to create a farm monitor-ing system that combines UAVs, LPWANs, and IoT technologies. This system aims to revolutionize current farm management practices and provide farmers with valuable data for their operations. To ensure the quality of soil in farming development, a system for monitoring soil quality using IoT technology was created. The collected data from the sensors was transmitted to the cloud for analysis through a multi-channel LoRaWAN® gateway integrated into a drone that uses dynamice mode. Various simulations and measurements were done to develop aerial communication based on LoRaWAN®, and the UAV path planning was optimized to improve the efficiency of data collection.

Internet of things-based indoor smart hydroponics farm monitoring system

Internet of things (IoT)-enabled systems are being used to monitor, control, and optimize agricultural systems like hydroponics. Optimization of these systems requires monitoring of various growth factors for plants which will also require multiple sensors. With the number of wireless sensors needed to track the conditions of the hydroponics system, the wireless sensor network will also grow in size and complexity. In this paper, a hybrid IoT-based monitoring system, with both wired and wireless components, with a target indoor farm application is proposed. The system was implemented using controller area network for the wired component, IPv6 over low power wireless personal area network (6LoWPAN) for the wireless component, and Amazon Web Services for the cloud services. Network simulations were performed to compare the network performance of the proposed hybrid system and the purely wireless system. The simulations show that incorporating a wired system results in improvements such as the reduction of packet loss from 12.23% in the purely wireless system to 5.62% in the hybrid system with burst traffic and even down to 0.0% in the hybrid system with continuous traffic.

Integrated agriculture IoT based farm monitoring and management systems

Integrated agriculture IoT based farm monitoring and management systems

Integrated agriculture IoT based farm monitoring and management systems

Integrated agriculture IoT based farm monitoring and management systems

Seaweeds for the sustainable blue economy development: A study from the south east coast of Bangladesh

Bangladesh is a maritime country with an area of 118000 km2 which supports a large number of commercially important species. Currently seaweeds are considered as important component of blue economy development in Bangladesh and farming is practiced at small scale level (Hypnea spp, Caulerpha reacemosa, Gelidium sp. are cultured). For the expansion of the seaweed farming in Bangladesh, a complete understanding of social and economic status of current farmers is important. However, information on socio-economic status of seaweed industry in Bangladesh is very limited. Therefore, this study aimed to understand existing culture methods and their cost, marketing channel and problems with seaweed farming in south east coast of Bangladesh. We used questionnaire survey and focus group discussions to collect data from seaweed farmers, researchers, local community and entrepreneurs. The study found that farmers are currently practicing long-line and horizontal net methods for the seaweed farming in the south east coastal region. The study also found that these culture methods are economically profitable. Seaweeds are currently sold locally and a proper value chain for seaweed marketing is still missing. Existing seaweed farmers are facing the problems related to insufficient credits for starting seaweed farming, lack of proper guidelines for farming and processing of harvested seaweed. This study suggests that for industrial level expansion of seaweed production in Bangladesh a proper value chain, development of seaweed derived products, farm monitoring systems, smooth seed supply and information hubs are required.

IOT 기반을 이용한 양식장 모니터링 시스템에 대한 사례 연구

Recently, fishing production has been decreasing, and as a countermeasure, we intend to increase production through aquaculture. However, problems such as water pollution, climate change, increase in marine waste, slowdown in fish farm growth, and death of unknown fish groups are being derived from aquaculture. In addition, depending on the size of the fish farm, poor management, lack of manpower, and safety accidents in aquaculture are occurring. Therefore, this study aims to present directions for the fish farm monitoring system based on IOT in the ocean and help those who want to study the fish industry.

Fully Automated Aqua Farm Monitoring System

Water quality of pond is the major issue in the aquaculture. There will be several parameters which are cause for change in water quality. Some of them include turbidity, water level, temperature, pH values, ammonia etc. For the past few years people came up with different techniques and methods to monitor water quality. In our research we are focusing on the three major parameters i.e., Turbidity, Temperature and pH, which may constitute a major role in polluting the pond water. So by using IoT technology (using various) we have implemented a system to monitor the water parameters continuously. In addition to that we proposed a methodology where the data received from the sensor is analyzed and will trigger actions based on some conditions. Our system will be able to monitor the parameter in real-time continuously with the capability of record and analyze each reading in a more efficient way using things speak platform. In this work, we have outlined and actualized monitoring of pond health continuously utilizing Arduino, various Sensors like turbidity sensor, temperature sensor, humidity sensor, pH sensor. In this work, we have also implemented mobile notification system where a user can monitor the water condition using mobile through Wi-Fi within Wi-Fi range and through Internet from anywhere in the world.

Farm Monitoring and Mapping System for Sugarcane in Thailand

Farm Monitoring and Mapping System for Sugarcane in Thailand

Development of a management and monitoring system for a city farm

For any manager of an agro-industrial enterprise, the use of resources is used for the efficient use of resources: labor, land, working capital and other. For this reason, the production processes of agriculture are already almost completely mechanized around the world, which is the productivity of an enterprise. The next logical step in increasing the productivity of agricultural enterprises is the automation and digitalization of agricultural production. Therefore, in order to remain competitive with the world leaders in agricultural production, it is necessary to integrate IoT technologies. It is predicted that the introduction of modern automation technologies in the agro-industrial complex will increase yields more than the inventions of the tractor, herbicides and GMOs. City farming is gradually becoming such a solution. In some megacities, urban farms are built on the rooftops of buildings, and in some places there are entire skyscra¬pers with vertical farms. Experts say that the quality and environmental friendliness of products grown in this way is much higher than that of counterparts grown in a traditional way. In this regard, the relevance in creating an application for a smart farm has been identified. Such an application will allow you to be more attentive to all parameters of plant growth thanks to sensors for humidity, light, temperature and other data that can be transmitted to a smartphone. The purpose of the study is to develop a management and monitoring system for a city farm. Materials and methods. The mobile application is implemented using the Xamarin framework in C# and JavaScript. The firmware for the microcontroller is written in C. Results. The paper provides an overview of management and monitoring systems for smart farms, a methodological comparison of advantages and disadvantages. The architecture of the control and monitoring system was also discovered. Implemented a mobile application and software. The functionality and design of the mobile application has been tested on various devices. Conclusion. The city farm management and monitoring system has been successfully developed. As further scientific work, it is planned to develop a mathematical model for more optimal farm management. This work can be useful in the field of decentralized agriculture.

Remote Farm Monitoring and Irrigation System

The research deals with the intelligent irrigation system using the Internet of Things (IoT) via Low cost and low power system on chip microcontrollers including integrated Wi-Fi with dual-mode Bluetooth ESP32. The objectives of this project are to investigate the concept of an intelligent irrigation system using the Internet of Things, to develop a system using the aforementioned controller that processes data from the soil sensor that automatically irrigates the plant and analyzes the soil status of the plants. In real-time via the smartphone connected to the Internet. The study scope focuses on cropping and horticulture. Sensors had to be installed for each plant as it was necessary to know the condition of the soil. A water pump must also be added to each plant to save water. This project requires the Blynk application which is a platform with IOS and Android apps to control Arduino, Raspberry Pi and the likes over the Internet. It’s a digital dashboard where you can build a graphic interface for your project by simply dragging and dropping widgets. Software on smartphone and hardware implementation which can detect environment condition using (DHT22: Temperature and Humidity sensor) sensor and soil moisture sensor. The results of this paper are based on the experiments performed.

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms.

Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).

Performance Analysis of Wi-Fi and LoRa Technology and its Implementation in Farm Monitoring System

One of the most interesting technology named Internet of Things made the world to move in smarter way. IoT is an idea of associating devices to internet which works automatically without human presence. Along with IoT different wireless technologies greatly supports in communication. Wide range communication and short range communication are made possible using IoT. These communications through IoT is done via technologies like Wi-Fi, Bluetooth, ZigBee, LoRa, NB-IoT, etc. In agriculture sector, these technologies helps in monitoring environmental factors in smarter way which yields more efficient crops but it works for only certain range. Some of the long range communicating technologies are NB-IoT, LoRa. Low Power wide area network will provide long range communication at low power consumption. LoRa is used to monitor the environmental factors in two different areas. Data from different areas is collected using LoRa device. Here, the comparisons in features of Wi-Fiand LoRa is graphically presented. Features like power, time, data rate for both Wi-Fi and LoRa has been analysed in this project. The data collected here is environmental factors such as moisture, temperature, humidity, motion and light. This project describes the features of Wi-Fi vs LoRa in agriculture for communication.

Automated Papaya Farm Monitoring system using Unmanned Aerial Vehicle (UAV)

Papaya Farm monitoring plays a key role in taking the required early-stage steps to increase the efficiency of harvesting. Using Unmanned aerial vehicles (UAVs) we can increase the efficiency of faming and get maximum yield. Improving performance Unmanned aerial vehicles (UAVs) offer a quick and reliable means of collecting data from difficult to reach large farms. Awareness of the latest usability and technologies used in UAVs and the techniques of programming used to process images taken from UAVs. The approaches used to evaluate the stage of development with available algorithms. To plants, local binary patterns, distance transformation, and watershed segmentation methods are applied to pictures. Using YOLOv4 architecture trine the system to program to detect the plats and find the deficiency in it. this will help to monitor the large farm very easy. Papaya is one of the plants that have the we can find the deficiency of the tree by using monitoring the arial leaf images. at proper care of the plant yielding is can be improved. here with the help of ML (machine learning) the program can be done. using python, we are going to trine the system and gest the result from the program. making the use of available algorithms to modify the performance the get the best result up to loss parentage of 3. The available free were coding yolo is used to performing the task. Here we are using the lasted vision of YOLOv4. that improve the accuracy up to 97%. the increasing the iterations making the result best, here we use 3600 iterations for this project.

Assessing technical efficiency of vegetable farms in North Macedonia

Farm economic performance measuring is important for farm management purposes and for assessing the impact of agricultural policy measures. Efficiency studies enable assessment of farm performance since highly efficient farms are considered to have higher potential for operating in a changing economic and technological environment. The aim of this paper is to assess the technical efficiency of vegetable farms in North Macedonia, utilising Data Envelopment Analysis, as a non-parametric approach estimating the relative efficiency of decision making units producing multiple outputs and using multiple inputs. The analysis is based on Farm Monitoring System data including 75 vegetable farms in 2011 survey. Apart from baseline scenario, additional scenarios include available agricultural policy support in 2011 and 2016. The average technical efficiency of vegetable farms is 0.62 considering constant returns to scale, i.e. 0.71 and 0.75 considering variable returns to scale (in output and input oriented perspective, respectively). With better farm management, technical efficiency score can be improved on average by 25%. Technical efficiency increases slightly with subsidies, on average by 2 to 3%. Analyses based on farm level data, are valuable when estimating the effects of implementation of agricultural policy as well as when creating new policy interventions.

Freshwater shrimp farm monitoring system for Bangladesh based on internet of things

SummaryThe freshwater shrimp (Macrobrachium rosenbergii) fisheries sector is of vital significance to the economy of Bangladesh. This sector plays an important role in supplying nutrition, employment creation, poverty alleviation, and overseas currency acquisition. Rapid population growth, environmental pollution, and global warming are reducing the production level of the fisheries (freshwater shrimp) sector. There are various environmental metrics, such as acidity and salinity level of water, turbidity, dissolved oxygen level, and so on, which play a vital role in the effective production of shrimps. It is hence paramount environmental and production parameters be constantly monitored to ensure quality in the production chain. Nevertheless, old‐fashioned (ie, manual) monitoring systems are cumbersome, time‐consuming, and never performed in real time. Therefore, a constant and computerized monitoring system is the only way to handle those metrics efficiently and in real time. In order to address these problems, a real‐time freshwater shrimp farm monitoring system is presented in this article. This monitoring system integrates technologies such as microcontroller‐based physical devices, internet of things, and web applications, that allow users to remotely monitor a shrimp farm, as well as to receive alerts when an out‐of‐range water parameter (ie, temperature, pH, dissolved oxygen, salinity level, and turbidity of water) is detected. The physical implementation of this system consists of a set of sensors that allow for collecting data about the water metrics of shrimp farm. This system was evaluated to test its effectiveness in terms of the size, weight, and the percentage of survival of the shrimp achieved when the shrimp culture pond is monitored by this system.