Purpose: The rapidly growing demand for food due to rapid population growth in East Africa is one of the challenging issues and the sustainable way of tackling it, is to enhance the agriculture activities to satisfy the need of increasing farm productivity. However, the climate change, limited water resources and poor soil fertility reduces crops yields. In attempt to solve these challenges, Internet of thing (IoT) in conjunction with artificial intelligence (AI) techniques is increasingly being used in agriculture sector. This study investigates an integration of IoT and a deep learning (DL) driven solution for smart irrigation and fertigation by assessing soil nutrients and soil water content dynamics in Eastern province of Rwanda for optimization of these scare resources while increasing yields productivity. Methodology: The research data for analysis was collected from KABOKU-KAGITUMBA irrigation scheme, and data on soil moisture and soil nutrients was gathered over a six-month period from 36 sensor nodes that were installed in approximately 70 hectares with 6 pivots for irrigation. The collected data in real time by sensors was sent to an IoT platform and incorporated with the forecasted weather information there after a deep learning based model used to predict when to irrigate and when to fertigate and the notification sent to farmer with recommendations. The irrigation valves were automatically actuated based on the predictions. The study's main software tools for gathering, displaying, and analyzing real-time data streams were Things Speak, Tensor Flow Lite, and the Arduino Software (IDE). A prototype was finally implemented effectively. Findings: The resulting model showed that can perform well with an accuracy of 91.7% and it can work well when deployed in the remote area with minimum internet connection. Unique Contribution to Practice: since the currently technologies used in irrigation and fertilization are manual or based on threshold values for automatic irrigation, we recommend the implementation of this solution since it will guarantee data-driven farming, which will help to protect the environment and ensure the optimization use of water resources. Additionally, this will result in lower operating cost, which will raise earnings.
Purpose: Since the breakout of COVID-19 pandemic, various preventive measures have been put in place by WHO to prevent the spread of this disease. However, some people are unaware or less likely to follow rules regarding hygiene, physical distancing, properly wearing of face mask and body temperature measurement. One of the best solutions to this challenge is the introduction of the internet of things (IoT) technology to assist in implementation of the preventive measures. This paper presents an IoT-enabled solution that uses Fuzzy logic controller to assess the risks of being COVID-19 infected and monitor environment conditions in the public hall to limit the spread of the coronavirus. Methodology: The proposed model employed sensors to measure in real-time the body temperature, hand sanitization, wearing of face mask, room ventilation and IP camera controlled by Fuzzy logic controller for decision making. In addition, it uses raspberry-pi for processing and data transmission to the cloud, liquid crystal display (LCD) for displaying data and web application was developed for user interface. The resulting sensor measurements were simulated using MATLAB software and the system made automatic decisions. Findings: A prototype was implemented effectively and the results obtained from the system were fast, accurate, efficient and cost effective when compared to other commercially available systems. Unique Contribution to Practice: The actual practice for implementing the preventive measures require the presence of the health care personnel (HCP) which is time consuming and risky for HCP. Therefore, this system works autonomously and effectively in monitoring and controlling the implementation of the COVID-19 preventive measures in the public hall.
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