In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several complex parameters. If we can reduce the complexity such greenhouses and improve the efficiency of their production management using intelligent schemes, technological greenhouses could become the expert assistants of farmers. In this paper, the main goal of the developed system is to measure the mushroom size and to count the amount of mushrooms. According to the results of each measurement, the growth rate of the mushrooms can be estimated. The proposed system also records the data of the mushrooms and broadcasts them to the mobile phone of the farmer. This improves the effectiveness of the production management. The proposed system is based on the convolutional neural network of deep learning, which is used to localize the mushrooms in the image. A positioning correction method is also proposed to modify the localization result. The experiments show that the proposed system has a good performance concerning the image measurement of mushrooms.

Fitness is important in people’s lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. Home fitness does not require large equipment but uses dumbbells, yoga mats, and horizontal bars to complete fitness exercises and can effectively avoid contact with people, so it is deeply loved by people. People who work out at home use social media to obtain fitness knowledge, but learning ability is limited. Incomplete fitness is likely to lead to injury, and a cheap, timely, and accurate fitness detection system can reduce the risk of fitness injuries and can effectively improve people’s fitness awareness. In the past, many studies have engaged in the detection of fitness movements, among which the detection of fitness movements based on wearable devices, body nodes, and image deep learning has achieved better performance. However, a wearable device cannot detect a variety of fitness movements, may hinder the exercise of the fitness user, and has a high cost. Both body-node-based and image-deep-learning-based methods have lower costs, but each has some drawbacks. Therefore, this paper used a method based on deep transfer learning to establish a fitness database. After that, a deep neural network was trained to detect the type and completeness of fitness movements. We used Yolov4 and Mediapipe to instantly detect fitness movements and stored the 1D fitness signal of movement to build a database. Finally, MLP was used to classify the 1D signal waveform of fitness. In the performance of the classification of fitness movement types, the mAP was 99.71%, accuracy was 98.56%, precision was 97.9%, recall was 98.56%, and the F1-score was 98.23%, which is quite a high performance. In the performance of fitness movement completeness classification, accuracy was 92.84%, precision was 92.85, recall was 92.84%, and the F1-score was 92.83%. The average FPS in detection was 17.5. Experimental results show that our method achieves higher accuracy compared to other methods.

Fine aerosols with a diameter of less than 2.5 microns (PM_2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM_2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM_2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM_2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM_2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM_2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM_2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

Restriction enzymes are used frequently in biotechnology. However, manual mining of restriction enzymes is challenging. Furthermore, integrating available restriction enzymes into different bioinformatics systems is necessary for many biotechnological applications, such as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Thus, in the present study, we developed the package REHUNT (Restriction Enzymes HUNTing), which mines restriction enzymes from the public database REBASE using a series of search operations. REHUNT is a reliable and open source package implemented in JAVA. It provides useful methods and manipulations for biological sequence analysis centered around restriction enzymes contained in REBASE. All available restriction enzymes for the imported biological sequences can be identified by REHUNT. Different genotypes can be identified using PCR-RFLP based on REHUNT for single nucleotide polymorphism (SNP), mutations, and the other variations. REHUNT robustly recognizes multiple inputs with different formats, e.g. regular DNA sequences, variation-in-sequence indicated by IUPAC code, as well as variation-in-sequence indicated by dNTPs format. Variations including di-, tri-, and tetra-allelic types and indel formats are also acceptable. Furthermore, REHUNT provides classified restriction enzymes output, including IUPAC and general sequence types, as well as commercial and non-commercial availabilities. REHUNT also enables analysis for high throughput screening (HTS) technologies. REHUNT is open source software with GPL v3 license and can be run on all platforms. Its features include: 1) Quick restriction enzymes search throughout a sequence based on the Boyer-Moore algorithm; 2) all available restriction enzymes provided and regularly updated from REBASE; 3) an open source API available of integrating all types of bioinformatics systems and applications; 4) SNP genotyping available for plant and animal marker-assisted breeding, and for human genetics; and 5) high throughput analysis available for Next Generation Sequencing (NGS). REHUNT not only to effectively looks for restriction enzymes in a sequence, but also available for SNP genotyping. Furthermore, it can be integrated into other biological and medical applications. REHUNT offers a convenient and flexible package for powerful restriction enzymes analyses in association studies, and supports high throughput analysis. The source codes and complete API documents are available at SourceForge: https://sourceforge.net/projects/rehunt/ , GitHub: https://github.com/yuhuei/rehunt , and at: https://sites.google.com/site/yhcheng1981/rehunt .

The Environmental Protection Administration of Taiwan’s Executive Yuan has set up many air quality monitoring stations to monitor air pollution in the environment. The current weather forecast also includes information used to predict air pollution. Since air quality indicators have a considerable impact on people, the development of a simple, fast, and low-cost method to measure the AQI value is a worthy topic of research. In this study, a method was proposed to estimate AQI. Visibility had a clear positive relationship with AQI. When images and AQI were compared, it was easy to see that visibility decreased with the AQI value increase. Distance is the main factor affecting visibility, so measuring visibility with images has also become a research topic. Images with high and low PM_2.5 concentrations were used to obtain regions of interest (RoI). The pixels in the RoI were calculated to obtain high-frequency information. The high-frequency information of RoI, RH, and true AQI was used for training via SVR, which was used to generate the model for AQI estimation. One year of experimental samples was collected for the experiment. Two indices were used to evaluate the performance of the proposed method. The results showed that the proposed method could be used to estimate AQI with acceptable performance in a simple, fast, and low-cost way.

The rapid development of the petrochemical industry has caused great harm to the environment. Fine suspended particles with a diameter of less than 2.5 microns cause serious health problems when inhaled air of high concentration of PM2.5. Therefore, an estimate of the concentration of PM2.5 is sought. However, it generally requires expensive instruments installed in an air quality monitoring station and a professional to operate them. In addition to the expensive cost, the instruments require a high maintenance fee and are restricted by geographical location. To eliminate the difficulty, this paper presents a low-cost and effective approach to estimate the concentration of PM2.5 using image processing schemes. The proposed approach consists of four stages. First, images with different concentrations of PM2.5 were taken and the related relative humidity (RH) was collected. Second, an automatically selected region of interest (RoI) was used to extract two features from an image, namely high-frequency information and transmittance by an improved dark channel. Third, the two extracted features, together with the RH measurement, were used to build a support vector regression (SVR) model. Fourth, the SVR model was applied to estimate the concentration of PM2.5, whose performance was then evaluated and compared with four simple regression models and a modified reported SVR method. In the given data set, the proposed method outperforms the comparison methods in terms of R2 and root mean squared error. The best performance of our method reaches R2=0.816 which is generally satisfactory in related applications.