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.

Tea (Camellia sinensis) is one of the most consumed drinks across the world. Based on processing techniques, there are more than 15 000 categories of tea, but the main categories include yellow tea, Oolong tea, Illex tea, black tea, matcha tea, green tea, and sencha tea, among others. Black tea is the most popular among the categories worldwide. During black tea processing, the following stages occur: plucking, withering, cutting, tearing, curling, fermentation, drying, and sorting. Although all these stages affect the quality of the processed tea, fermentation is the most vital as it directly defines the quality. Fermentation is a time-bound process, and its optimum is currently manually detected by tea tasters monitoring colour change, smelling the tea, and tasting the tea as fermentation progresses. This paper explores the use of the internet of things (IoT), deep convolutional neural networks, and image processing with majority voting techniques in detecting the optimum fermentation of black tea. The prototype was made up of Raspberry Pi 3 models with a Pi camera to take real-time images of tea as fermentation progresses. We deployed the prototype in the Sisibo Tea Factory for training, validation, and evaluation. When the deep learner was evaluated on offline images, it had a perfect precision and accuracy of 1.0 each. The deep learner recorded the highest precision and accuracy of 0.9589 and 0.8646, respectively, when evaluated on real-time images. Additionally, the deep learner recorded an average precision and accuracy of 0.9737 and 0.8953, respectively, when a majority voting technique was applied in decision-making. From the results, it is evident that the prototype can be used to monitor the fermentation of various categories of tea that undergo fermentation, including Oolong and black tea, among others. Additionally, the prototype can also be scaled up by retraining it for use in monitoring the fermentation of other crops, including coffee and cocoa.

Artificial intelligence (AI) is a disruptive technology that has achieved remarkable breakthroughs across various domains and is reshaping our daily lives. However, AI’s resource-intensive, time-consuming, and domain-specific nature creates a significant divide between developed countries, with abundant resources, and developing countries, where computing power and datasets are limited. To bridge this gap, Tiny Machine Learning (TinyML), Transfer Learning (TL), and Domain Adaptation (DA) emerge as crucial approaches for implementing AI in resource-scarce environments. TinyML deploys compressed AI models on resource-constrained devices, including mobile devices and microcontrollers. TL leverages knowledge from one task to enhance performance on a related task, while DA, a sub-discipline of TL, applies models trained on one distribution to a related but different distribution. This paper presents a feasibility study on applying DA to develop a TinyML model for detecting and classifying diseases in domestic crops. We proposed a 2D-CNN model named "AgroNet," equipped with a domain classification head to predict the input domain, thus enhancing adaptability to different agricultural domains. We evaluated its performance with a TL-based 2D-CNN model and a traditional 2D-CNN baseline model in both source and target domains. The source domain dataset encompassed tea, apples, coffee, tomatoes, beans, and maize, while the target domain dataset included peas, cabbage, cassava, cowpea, and spinach—evaluation metrics comprised precision, f-measure, and accuracy. Results indicated superior performance in the source domain compared to the target domain. However, AgroNet exhibited satisfactory performance in the target domain, achieving an average score of over 0.7 across all metrics, while the baseline and TL-based models displayed significant performance drops, recording averages of less than 0.5 in most classes. The TL and baseline models’ reliance on static features limited their adaptability, particularly in the target domain characterized by diverse image characteristics. These findings underscore the necessity of extensive retraining for traditional approaches in new domains. Regarding memory and energy requirements, AgroNet consumed more resources than the other ML models, with 200MB and 500 joules expended over 61 seconds of training. We deployed the AgroNet model in an apple farm in Kericho County embedded in a mobile device. The energy and memory requirements were sufficient for execution on resource-scarce devices, as evidenced by the mobile devices, including embedded systems. The model recorded average results during real deployment with a significant drop in classification performance compared to simulation environments, likely due to noise introduced in the deployment environment, including background noise and differing lighting conditions. This study demonstrated the feasibility of DA for AI deployment in resource-constrained settings, as it can be trained on a source domain and applied in a comparable target domain after limited fine-tuning. This saves on computing costs and accelerates deployments. In the future, we will explore ensemble learning techniques, including boosting and bagging, and feature engineering methods, such as principal component analysis, feature selection, and data augmentation, to enhance machine learning classification performance.

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.