Tea is one of the most popular beverages in the world, and its processing involves a number of steps which includes fermentation. Tea fermentation is the most important step in determining the quality of tea. Currently, optimum fermentation of tea is detected by tasters using any of the following methods: monitoring change in color of tea as fermentation progresses and tasting and smelling the tea as fermentation progresses. These manual methods are not accurate. Consequently, they lead to a compromise in the quality of tea. This study proposes a deep learning model dubbed TeaNet based on Convolution Neural Networks (CNN). The input data to TeaNet are images from the tea Fermentation and Labelme datasets. We compared the performance of TeaNet with other standard machine learning techniques: Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). TeaNet was more superior in the classification tasks compared to the other machine learning techniques. However, we will confirm the stability of TeaNet in the classification tasks in our future studies when we deploy it in a tea factory in Kenya. The research also released a tea fermentation dataset that is available for use by the community.

Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions. These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO, CO₂ and O₂ vis-à-vis the Estimated Fire Intensity Prediction (EFIP). Results show that, using fuzzy logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws, policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places.

Agriculture contributes to the economies of many developing countries. Tea is the most popular crop in Kenya as it contributes majorly to her economy. Among the various stages of processing tea, fermentation is the most important as it determines the final quality of the processed tea. Presently, the process of monitoring is done manually by tea tasters by tasting, smelling, and touching tea which compromises the quality of tea. In this paper, a deep learner dubbed “TeaNet” is deployed in Edge and Fog environments for real-time monitoring of tea fermentation. We power the system using a Photovoltaic (PV) energy source to overcome the challenge of unreliable power supply from the grid. Further, the energy consumption of the solution is reduced by applying duty cycling, where idle components are designed to sleep. We used the Analysis of variance (ANOVA) and Post-hoc for data analysis. From the results, Edge registered the lowest latency compared to the Cloud and Fog environments. During deployment of the energy-optimized model, 50.6559Wh amount of energy was saved. This study recommends that the task offloading model proposed in this study be explored in offloading tasks in other fields.

Tea is currently the most popular beverage after water. Tea contributes to the livelihood of more than 10 million people globally. There are several categories of tea, but black tea is the most popular, accounting for about 78% of total tea consumption. Processing of black tea involves the following steps: plucking, withering, crushing, tearing and curling, fermentation, drying, sorting, and packaging. Fermentation is the most important step in determining the final quality of the processed tea. Fermentation is a time-bound process and it must take place under certain temperature and humidity conditions. During fermentation, tea color changes from green to coppery brown to signify the attainment of optimum fermentation levels. These parameters are currently manually monitored. At present, there is only one existing dataset on tea fermentation images. This study makes a tea fermentation dataset available, composed of tea fermentation conditions and tea fermentation images.

Climate change is one of the most significant challenges to every country's development, ravaging havoc on the lives of all people on this planet. Researchers have raised numerous research and studies of strategies for tracking climate change. The current climate change tracking method in Rwanda employs a weather station model, in which numerous fixed weather stations are installed throughout the country; however, due to its immobility, this process cannot cover the entire country. With the lack of advanced methodologies and technology, the process of climate change tracking has become extremely expensive and suffered inaccuracies due to a lack of proper knowledge of analyzing collected data, and the lack of specific accurate hardware. Throughout this research, with the use of the MQ-135 and DHT11 sensors, ESP8266 collects carbon dioxide gas and temperature/humidity respectively and other component include a push button for detecting the current season. ESP8266 is programmed to send data over MQTT protocol, which uses Wi-Fi capability to send data to MQTT Broker. Using the MQTT protocol's Publish/Subscribe criteria, node-red subscribes to the topics defined in the MQTT broker to obtain data, which is then sent to MongoDB for permanent storage and also fed into the machine learning model for climate change/warming prediction. Different algorithms are used to evaluate this model. As result, Random Forest classifier approves itself to be the best model in evaluating the built model. This study shows that the increase in carbon dioxide gas leads to the gradual increase in the environmental temperature. Finally, the prediction clarifies that if no measures are taken presently, the climate change in Rwanda's Industrial zone will be dominated by warming periods in the future.