Integration of Altimeter and GPS Sensors on IoT-Based Drone Delivery System (DDS)

As the trend of online shopping increased, the demand for cheaper and quicker delivery became apparent. Hence, the proposed IoT-based DDS is targeted as an alternative to decreasing the price and time taken to perform the delivery operation. This system will extract the current altitude and position of the drone via the MPL3115A2 altimeter sensor and GY-NEO6MV2 GPS module attached to the drone. The ESP32 NodeMCU would collect sensor measurement and transmit this information to the cloud server hosted on AWS EC2 virtual machine wirelessly via Wi-Fi. The recipient and operator can access the dashboard through the cloud server to perform operations such as tracking, registering, confirming, and dropping parcels. The servo motor attached to the drone would automatically facilitate parcel dropping through a simple release mechanism

Vision-Based Monitoring (VBM) for Plant Quality and Control System

This research paper designs and implements a prototype of VBM system by using a line follower robot car for indoor hydroponic plant monitoring. The proposed robot car was using line following IR sensors to follow the track line. The robot car controlled by Arduino Mega 2560. The robot is connected to Raspberry Pi for image processing purpose. The image processing can be archived by implementing the object surface area measurement method. The python module OpenCV, Numpy, matlabplt, glob, docx and OS were involved for image preprocessing and surface area calculation respectively. Hence, a document in pdf format will generate and send to the NAS system through WiFi. In fact, more actions can be taken as saved the result in PDF format as other workers able to view the entire results, taking some actions for example add on comments, signing and recording. Therefore, the document copy on board will be deleted to reduce occupied memory resources. The NAS system was builded by Raspberry Pi and a hard disk. It is connected to router via ethernet cable to gain stable internet connections. After that, the workers can link the NAS folder and have review on their desktop remotely

Study of LED Radiation Effects on Insect Phototaxis Response for the Development of Light-Based Pest Trap

: Commercial agriculture is a resource intensive industry that employs significant amounts of agrochemicals to maintain a high yield output. Excessive application of insecticides has detrimental impacts on human health, environment and long-term sustainability. One particular area of interest is in exploring how insects are attracted to specific bands of the visible light spectrum. This paper details the study of several light conditions using Light Emitting Diodes (LEDs) to determine its phototaxis response on the common grasshopper, garden moth and lava beetle. The results conclude that all three insects exhibit positive phototaxis response on wavelengths around 491nm and below, which encompasses ultraviolet, blue and green. The radiant intensity experiment revealed a threshold response of 2.8mW/m2, or 25m in reciprocal distance

Design and Development of Smart Irrigation and Water Management System for Conventional Farming

The intention of this project is to develop a smart irrigation and water management system for conventional farming. The project is conducted mainly to improve the irrigation scheduling and also to solve the over watering and under watering issues in traditional irrigation system. These problems can be solved by implementing soil moisture sensor as a smart component in the irrigation system. Smart irrigation system with the implementation of sensory-based system will be able to provide a proper irrigation scheduling, by monitoring the soil and weather condition of the farm. In this project, the sensory system consists of soil moisture sensor, temperature sensor and light intensity sensor, which basically used to monitor soil moisture level, temperature level, and light intensity level at the separate test area. Arduino Mega 2560 microcontroller will process the data from these sensors and a proper irrigation scheduling will be developed based on the data collected. Type of irrigation system that been used in this project is a sprinkler system because it has high uniformity of water distribution to the plant, which able to spread water efficiency and further optimize the water usage during irrigation process. Ultrasonic sensor is also implemented in the system to measure the amount of water used in each irrigation process performed. An offline data storage will be implemented in this project using a micro SD card module, which all the essential information such as sensory system readings and the amount of water used will be recorded and stored into a micro SD card. Thus, it allows user to monitor their farm's condition, and also gives a better view on what is really happening at their farm

Newly calibrated analytical models for soil moisture content and pH value by low-cost YL-69 hygrometer sensor

Soil moisture content and soil pH values, especially in the surface layers of the soil play a significant role in agriculture production in hydrological processes, meteorology observation and horticulture study. This paper proposes an analytical model of estimating the moisture content, acidity and alkalinity in soil through measurement of soil resistivity by an on-site, real-time method. In this study, a low-cost soil resistivity sensor YL-69 was used to characterize the dynamics of soil moisture content and its pH value. Laboratory calibrated regression models were developed for three types of soils, i.e., clay, thionic, and fine sandy loamy to evaluate the effect of soil temperature and the depths of sensor on the apparent soil moisture content and pH value with a single resistive sensor. The prediction models were found to be accurate with the accuracy of 98%. The findings obtained from this study could provide inexpensive and accurate simultaneous soil moisture content and pH value measurement

Pairwise Feature Learning for Unseen Plant Disease Recognition

With the advent of Deep Learning, people have begun to use it with computer vision approaches to identify plant diseases on a large scale targeting multiple crops and diseases. However, this requires a large amount of plant disease data, which is often not readily available, and the cost of acquiring disease images is high. Thus, developing a generalized model for recognizing unseen classes is very important and remains a major challenge to date. Existing methods solve the problem with general supervised recognition tasks based on the seen composition of the crop and the disease. However, ignoring the composition of unseen classes during model training can lead to a reduction in model generalisation. Therefore, in this work, we propose a new approach that leverages the visual features of crop and disease from the seen composition, using them to learn the features of unseen crop-disease composition classes. We show that our proposed method can improve the classification performance of these unseen classes and outperform the state-of-the-art in the identification of multiple crop-diseases

Model Study for Outdoor Data Transmission Performance

Network performance for outdoor transmissions are varies according to different environment especially when there was obstacle along the transmission path and therefore the expected performance for outdoor transmissions is unpredictable. These unpredictable performances of outdoor data transmission forced Internet Service Providers (ISPs) to use stronger or better routers to establish the data transmission link. So, the initial investment for the project in a rural area will be higher than urban area due to long-range outdoor transmissions. Normally, this cost will be absorbed by the customers, who will be paying higher subscription fees. But, the income of residents in rural areas is lower than urban areas and the ISP is forced to absorb the extra initial investment by prolonging the projects’ breakeven point. Therefore, this study aimed to a model for estimating average data speed for outdoor Wi-Fi data transmission. The average data speed of outdoor Wi-Fi data transmission was modelled by conducting experiments that simulate real-world wireless transmission. The experiments were conducted by selecting suitable transmitter and receivers. The obstacle used in the experiment was made from aluminum plate which has high attenuation for wireless signals. The experimental data were further validated with actual testing with real-world obstacles. The average percentage error for the outdoor Wi-Fi average data speed model was 24.97%

Development and Application of Outdoor Router Cost Estimation with Parametric Modelling Technique

Internet development is a challenging issue among Internet Server Providers (ISPs) and researchers due to high investment cost and unforeseen risk. The internet accessibilities of those rural areas are low and seem disconnected from the society. Rural areas unable to enjoy the benefits from high-speed Internet. Rural internet development is not prioritized because of low population density and return of investment from urban area development is more favorable. Outdoor equipment such as router, antennas and access points are the main components in Internet development. The accuracy of various cost estimation model is depending on the availability of raw data and data analysis techniques. There is no accurate model that allow ISPs to estimate the cost of outdoor routers for Wireless Fidelity (Wi-Fi) transmission. Those estimations can assist ISPs in risk management and reduce the total project cost. Therefore, this paper aimed to produce and demonstrate a suitable outdoor router cost estimation model. Friis transmission equation and link budget equation were used in this model. Suitable key parameters were selected by using P-value regression analysis. Original key parameters and calculated unique key parameters were utilized in this model to provide better performance and realistic estimated cost. This paper also demonstrated the usage of outdoor router cost estimation model under long-range and short-range wireless data transmission

Cost estimation methods for internet infrastructure deployment in Rural Sarawak: a review

In rural Sarawak, the internet accessibility is low due to unreliable power grids to support telecommunication network and large geographical area. The risk for network infrastructure implementation is high for internet service provider (ISP), thus more practical and accurate cost estimation methods should be used. This paper reviews different types of cost estimation methods and the accuracy and feasibility of each methods are discussed and compared for network infrastructure implementation in rural Sarawak. The unique characteristics of rural Sarawak are considered in this work, including the topography, development of rural areas and acceptance of new technologies. Different cost estimation methods are identified for different senarios and availability of data