Classification of Fresh and Frozen-Thawed Beef Using a Hyperspectral Imaging Sensor and Machine Learning

The demand for safe and edible meat has led to the advancement of freeze-storage techniques, but falsely labeled thawed meat remains an issue. Many methods have been proposed for this purpose, but they all destroy the sample and can only be performed in the laboratory by skilled personnel. In this study, hyperspectral image data were used to construct a machine learning (ML) model to discriminate between freshly refrigerated, long-term refrigerated, and thawed beef meat samples. With four pre-processing methods, a total of five datasets were prepared to construct an ML model. The PLS-DA and SVM techniques were used to construct the models, and the performance was highest for the SVM model applying scatter correction and the RBF kernel function. These results suggest that it is possible to construct a prediction model to distinguish between fresh and non-fresh meat using the spectra obtained by purifying hyperspectral image data cubes, which can be a rapid and non-invasive method for routine analyses of the meat storage state

Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments

Mid-Infrared Lifetime Imaging for Viability Evaluation of Lettuce Seeds Based on Time-Dependent Thermal Decay Characterization

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Characteristics of Vibration Transmissibility for Corrugated Paperboard

Corrugated fiberboard are typically lightweight and inexpensive with high stiffness?to?weight and trength?to?weight ratios as an environmental?friendly packaging materials. In order to optimize the application of corrugated paperboard for packaging cushioning materials, it is essential that both a vibration transmissibility test and drop shock test be completed for the transmissibility?frequency curve and dynamic cushion curve respectively. This paper deals with the characterization of properties of corrugated paperboard relevant to its application for protective packaging in transportation, such as vibration transmissibility, resonant frequency, damping ratio, and maximum dynamic stress. The main feature of the paper is the evaluation on the vibration transmissibility of corrugated paperboard with corrugation shape and the equilibrium atmospheric conditions by a sinusoidal sweep vibration test. Additionally, damping ratio and maximum dynamic stress were evaluated by linear vibration theory. All the works provide basic data relevant to its application for protective packaging in transportation

CNN-based object detection and growth estimation of plum fruit (Prunus mume) using RGB and depth imaging techniques

Abstract Modern people who value healthy eating habits have shown increasing interest in plum (Prunus mume) fruits, primarily owing to their nutritiousness and proven efficacy. As consumption increases, it becomes important to monitor work to prevent Prunus mume fruits from falling out. Moreover, determining the growth status of Prunus mume is also crucial and is attracting increasing attention. In this study, convolutional neural network (CNN)-based deep learning object detection was developed using RGBD images collected from Prunus mume farms. These RGBD images consider various environments, including the depth information of objects in the outdoor field. A faster region-based convolutional neural network (R-CNN), EfficientDet, Retinanet, and Single Shot Multibox Detector (SSD) were applied for detection, and the performance of all models was estimated by comparing their respective computing speeds and average precisions (APs). The test results show that the EfficientDet model is the most accurate, and SSD MobileNet is the fastest among the four models. In addition, the algorithm was developed to acquire the growth status of P. mume fruits by applying the coordinates and score values of bounding boxes to the depth map. Compared to the diameters of the artificial Prunus mume fruits used as the experimental group, the calculated diameters were very similar to those of the artificial objects. Collectively, the results demonstrate that the CNN-based deep learning Prunus mume detection and growth estimation method can be applied to real farmlands

Moisture Content Measurement of Broadleaf Litters Using Near-Infrared Spectroscopy Technique

Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. The maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all of the candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture

Calibration and Image Processing of Aerial Thermal Image for UAV Application in Crop Water Stress Estimation

Crop monitoring is a very important area of precision agriculture and smart farming. Through an accurate monitoring, it is possible to more efficiently manage the irrigation, fertilization, and pest control. In this study, we propose aerial thermal image calibration method and thermal image processing techniques to analyze the water stress level of fruit trees under different irrigation conditions. The calibration was performed using Gaussian process regression, and it was demonstrated as an appropriate regression method because it satisfied all requirements including the residuals’ normality, independence, and homoscedasticity. In addition, an appropriate image processing technique was necessary to selectively extract only the canopy temperature from the aerial thermal images, while excluding irrelevant elements such as the soil and other objects. For the image processing techniques, three methods (Gaussian mixture model, Otsu binarization algorithm, and Otsu binarization algorithm after Gaussian blurring) were employed. The Gaussian mixture model provided the highest accuracy and stable results for the extraction of the canopy temperature. After the aerial thermal images were subjected to calibration and image processing, the degree above nonstressed canopy (DANS) water stress index was calculated for the fruit trees under different water supply conditions. The distribution of the DANS water stress index was similar to the distribution of the canopy temperature and inversely proportional to the amount of supplied water content. Therefore, we expect that the DANS water stress index, calculated using the calibration and image processing techniques proposed in this study, can be a reliable measure for the estimation of the water stress of crops for the application of aerial infrared techniques to remote sensing

Automatic Pest Counting from Pheromone Trap Images Using Deep Learning Object Detectors for Matsucoccus thunbergianae Monitoring

The black pine bast scale, M. thunbergianae, is a major insect pest of black pine and causes serious environmental and economic losses in forests. Therefore, it is essential to monitor the occurrence and population of M. thunbergianae, and a monitoring method using a pheromone trap is commonly employed. Because the counting of insects performed by humans in these pheromone traps is labor intensive and time consuming, this study proposes automated deep learning counting algorithms using pheromone trap images. The pheromone traps collected in the field were photographed in the laboratory, and the images were used for training, validation, and testing of the detection models. In addition, the image cropping method was applied for the successful detection of small objects in the image, considering the small size of M. thunbergianae in trap images. The detection and counting performance were evaluated and compared for a total of 16 models under eight model conditions and two cropping conditions, and a counting accuracy of 95% or more was shown in most models. This result shows that the artificial intelligence-based pest counting method proposed in this study is suitable for constant and accurate monitoring of insect pests

Automatic pest counting from pheromone trap images using deep learning object detectors for matsucoccus thunbergianae monitoring

Simple Summary The black pine bast scale, Matsucoccus thunbergianae, is a forest pest that causes widespread damage to black pine; therefore, monitoring this pest is necessary to minimize environmental and economic losses in forests. However, monitoring insects in pheromone traps performed by humans is labor intensive and time consuming. To develop an automated monitoring system, we aimed to develop algorithms that detect and count M. thunbergianae from images of pheromone traps using deep-learning-based object detection algorithms. Object detection models based on deep learning neural networks under various conditions were trained, and the performances of detection and counting were compared and evaluated. In addition, the models were trained to detect small objects well by cropping images into multiple windows. As a result, the algorithms based on deep learning neural networks successfully detected and counted M. thunbergianae. These results showed that accurate and constant pest monitoring is possible using the artificial-intelligence-based methods we proposed. The black pine bast scale, M. thunbergianae, is a major insect pest of black pine and causes serious environmental and economic losses in forests. Therefore, it is essential to monitor the occurrence and population of M. thunbergianae, and a monitoring method using a pheromone trap is commonly employed. Because the counting of insects performed by humans in these pheromone traps is labor intensive and time consuming, this study proposes automated deep learning counting algorithms using pheromone trap images. The pheromone traps collected in the field were photographed in the laboratory, and the images were used for training, validation, and testing of the detection models. In addition, the image cropping method was applied for the successful detection of small objects in the image, considering the small size of M. thunbergianae in trap images. The detection and counting performance were evaluated and compared for a total of 16 models under eight model conditions and two cropping conditions, and a counting accuracy of 95% or more was shown in most models. This result shows that the artificial intelligence-based pest counting method proposed in this study is suitable for constant and accurate monitoring of insect pests.Y