Peaberries are a special type of coffee bean with an oval shape. Peaberries are not considered defective, but separating peaberries is important to make the shapes of the remaining beans uniform for roasting evenly. The separation of peaberries and normal coffee beans increases the value of both peaberries and normal coffee beans in the market. However, it is difficult to sort peaberries from normal beans using existing commercial sorting machines because of their similarities. In previous studies, we have shown the availability of image processing and machine learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest-neighbors (KNNs), for the classification of peaberries and normal beans using a powerful desktop PC. As the next step, assuming the use of our system in the least developed countries, this study was performed to examine their implementation in and the limitations of Raspberry Pi 3. To improve the performance, we modified the CNN architecture from our previous studies. As a result, we found that the CNN model outperformed both linear SVM and KNN on the use of Raspberry Pi 3. For instance, the trained CNN could classify approximately 13.77 coffee bean images per second with 98.19% accuracy of the classification with 64×64 pixel color images on Raspberry Pi 3. There were limitations of Raspberry Pi 3 for linear SVM and KNN on the use of large image sizes because of the system's small RAM size. Generally, the linear SVM and KNN were faster than the CNN with small image sizes, but we could not obtain better results with both the linear SVM and KNN than the CNN in terms of the classification accuracy. Our results suggest that the combination of the CNN and Raspberry Pi 3 holds the promise of inexpensive peaberries and a normal bean sorting system for the least developed countries.

The quality and uniformity of green coffee beans significantly influence the overall flavor and value of the product. In the coffee industry, automated flaws and bean-type identification offer numerous advantages. This study examines the effectiveness of multiple YOLO (You Only Look Once) models for identifying and classifying green coffee beans. Various YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and custom models, are compared with a focus on computational efficiency, accuracy, and speed. Utilizing a dataset of 4,032 training and 506 testing images encompassing diverse bean types, defects, and lighting conditions, we assessed the performance. The bounding boxes generated by our models accurately encompass coffee beans, with the background typically uniform and set to black. Our analysis reveals the superior performance of the custom-YOLOv8n model, which we meticulously customized for green coffee bean detection. This model achieved high precision, recall, f1-score, and mAP, demonstrating its potential for real-world implementation in coffee bean quality control systems. The customization process involved fine-tuning the model to focus on significant features relevant to green coffee bean detection and employing specific labeling strategies. Customization allows you to fine-tune the model to focus on important features relevant to green coffee bean detection. This sensitivity ensures that the model can effectively distinguish between different bean types and detect even subtle defects. This paper clarifies our primary objective of evaluating YOLO models’ performance in identifying and categorizing green coffee beans, with potential implications for enhancing efficiency and consistency in the coffee industry. A succinct key sentence underscores the benefits of our research for readers seeking efficient YOLO model selection and implementation in agricultural systems.

Background: In the realm of system biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time-series gene expression dataset. However, most of these techniques pose scalability concerns given their capability to consider only two to three regulatory genes over a specific target gene. Methods: To overcome this limitation, a novel inference method, LBF-MI, has been proposed in this research. This two-phase method utilizes limited Boolean functions and multivariate mutual information to reconstruct a Boolean gene regulatory network from time-series gene expression data. Initially, Boolean functions are applied to determine the optimum solutions. In case of failure, multivariate mutual information is applied to obtain the optimum solutions. Results: This research conducted a performance-comparison experiment between LBF-MI and three other methods: mutual information-based Boolean network inference, context likelihood relatedness, and relevance network. When examined on artificial as well as real-time-series gene expression data, the outcomes exhibited that the proposed LBF-MI method outperformed mutual information-based Boolean network inference, context likelihood relatedness, and relevance network on artificial datasets, and two real Escherichia coli datasets (E. coli gene regulatory network, and SOS response of E. coli regulatory network). Conclusions: LBF-MI’s superior performance in gene regulatory network inference enables researchers to uncover the regulatory mechanisms and cellular behaviors of various organisms.

Nowadays people have been moved so much into the modern technology that they really want an intelligent living environment along with intelligent objects which contain powerful infrastructure with the most desired features. According to various statistics, the number of smartphone users in 2017 was 2.32 billion around the world. In 2020, it will exceed 2.87 billion. Therefore, if organizations and industries are not investing in mobile applications to increase productivity and accessibility among their workforce, they are going to fall behind. Bangladesh government has also taken initiatives to implement the vision 2021 to build a digital Bangladesh. As part of this initiative, the ICT division of Bangladesh government has funded the development of android mobile applications for all government organizations. These applications provide easier access to information and many more facilities. The ultimate goal of this app is to facilitate the user with the process of searching essential information in an instant rather than flipping the pages of the existing paper-based diary of Sylhet Agricultural University (SAU) in Bangladesh with additional functionalities. The paper illustrates the features, development method, result, and uses of our android application named “SAU app”. There was no android application for SAU earlier. To embrace the digital world and to precede one step ahead of many others, we have come up with our online mobile application which can solve their problem during checking staffs’ information of SAU in a moment. Every person can also easily operate the system in home and aboard.

In the realm of System Biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time series gene expression dataset. However, most of these techniques pose scalability concerns given their capability to consider only 2 to 3 regulatory genes over a specific target gene. To overcome this limitation, a novel inference method, LBF- MI, has been proposed in this research. This two-phase method utilizes limited Boolean functions and multivariate mutual information to reconstruct a Boolean gene regulatory network from time-series gene expression data. Initially, Boolean functions are applied to determine the optimum solutions. In case of failure, multivariate mutual information is applied to obtain the optimum solutions. This research conducted a performance-comparison experiment between LBF-MI and two other methods: Context Likelihood Relatedness, and Relevance Network. When examined on artificial as well as real time-series gene expression data, the outcomes exhibited that the proposed LBF-MI method outperformed Context Likelihood Relatedness and Relevance Network on artificial datasets, and two real Escherichia Coli datasets (E. coli gene regulatory network, and SOS response of E. coli regulatory network).