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.

The central role of water access for agriculture is a clear challenge anywhere in the world and particularly in areas with significant seasonal variation in rainfall such as in Eastern and Central Africa. The combination of modern sensor technologies, the Internet, and advanced irrigation equipment combined in an Internet of Things (IoT) approach allow a relatively precise control of agricultural irrigation and creating the opportunity for high efficiency of water use for agricultural demands. This IoT approach can thereby increase the resilience of agricultural systems in the face of complex demands for water use. Most previous works on agricultural IoT systems are in the context of countries with higher levels of economic development. However, in Rwanda, with a low level of economic development, the advantages of efficient water use from the application of IoT technology requires overcoming constraints such as lack of irrigation control for individual farmers, lack of access to equipment, and low reliability of power and Internet access. In this work, we describe an approach for adapting previous studies to the Rwandan context for rice (Oryza sativa) farming with irrigation. The proposed low cost system would automatically provide irrigation control according to seasonal and daily irrigational needs when the system sensors and communications are operating correctly. In cases of system component failure, the system switches to an alternative prediction mode and messages farmers with information about the faults and realistic irrigation options until the failure is corrected. We use simulations to demonstrate, for the Muvumba Rice Irrigation Project in Northeast Rwanda, how the system would respond to growth stage, effective rainfall, and evapotranspiration for both correct operation and failure scenarios.

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.

In developing countries, many customers do not get good quality of electricity power supply due to frequent and prolonged power fluctuations/cuts. Distribution transformer (DT) is a service transformer that provides the final voltage transformation in the electric power distribution system, stepping down the voltage to the level used by the customers. Monitoring and control of DT (as a crucial and expensive asset in the network) is a key enabler of power stability to consumers. A power utility is said to be a business oriented with good image representation if it delivers a reliable and affordable electricity. Modern technologies such as the Internet of Things (IoT) offer a wide range of applications in the energy sector to smoothly monitor, control and optimize processes. Currently, many energy companies in developing countries are not yet implementing the remote system to control and monitor the secondary side of DTs and timely get the notifications of fluctuations/abnormalities occurred on those DTs. That is why it is still challenging and time consuming to intervene urgently and do the necessary actions to prevent severe and prolonged power cuts/fluctuations and safeguard the damage of DTs themselves with customer's appliances connected on those DTs. The secondary side of DT is the one connected directly and supply power to the customers. For this reason, we developed an affordable IoT system that automatically detects the abnormalities/fluctuations of three core technical parameters of DT (which are voltage, current and temperature) using current sensors, voltage sensors, and temperature sensor with ATmega 328P Microcontroller to collect and process data from sensors connected to DT system. Once one or all of those technical parameters become abnormal, the system cut off automatically the secondary side of DT in 2 seconds to isolate and protect the customers’ load with safeguarding DT itself using a power relay. At the same time, GSM/GPRS module uploads the sensed abnormal data to the cloud storage, displays them on web-based application for visualization, and sends the corresponding short message service (sms) to notify the issue to the authorized person in 5 seconds for speeding up the interventions and power restoration. In case there is a movement related to the vandalism in the compound of DT, a PIR sensor detects the human motion then a camera takes the related picture and send it to the utility with the corresponding sms. A buzzer generates an audio signaling to warn the culprit/criminal until he left the site. If there is no abnormality detected, the system keeps sensing without sending the data to the cloud. We can open and close remotely the secondary side of DT and buzzer. This system is powered using a rechargeable battery.