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This study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change; in particular, it evaluates the effectiveness of various non-destructive remote sensing platforms (RGB, thermal imaging, and hyperspectral imaging) and AI techniques (machine learning, deep learning, ensemble methods, GAN, and XAI) in monitoring and predicting crop water stress. The analysis focuses on variability in precipitation due to climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity. Furthermore, this study is expected to contribute to improving sustainable agricultural practices and mitigating the negative impacts of climate change on crop yield and quality. [ABSTRACT FROM AUTHOR]

Mercury bromide (Hg2Br2) has been used to develop acousto-optic tunable filters (AOTFs) because it has several advantages, including a high refractive index, a broad optical bandwidth, and a relatively high figure of merit. Therefore, the measurement of its birefringence is a highly important factor for ensuring AOTF quality. However, for single crystals, it is difficult (at the millimeter scale) to quantify the birefringence using an ellipsometer, as this equipment is only designed to conduct measurements on thin films. In this study, a simple birefringence measurement system for Hg2Br2 was developed based on Brewster's angle at the millimeter scale. The planar distributions of the Hg2Br2 crystal along the (100), (010), and (001) planes were used in the experiments. The developed measurement system can measure the reflected light intensity of the Hg2Br2 crystal depending on the incidence angles (rotations at 0.01125° steps) and can calculate the ordinary and extraordinary refractive indices and birefringence. The calculated birefringence of the Hg2Br2 crystal was 0.8548; this value exhibits an error of 0.64% compared with a value of 0.86 reported in the literature. The developed measurement system demonstrates the ability to be used to evaluate the quality of birefringent single crystals. [ABSTRACT FROM AUTHOR]

In this study, conventional machine learning and deep leaning approaches were evaluated using X-ray imaging techniques for investigating the internal parameters (endosperm and air space) of three cultivars of watermelon seed. In the conventional machine learning, six types of image features were extracted after applying different types of image preprocessing, such as image intensity and contrast enhancement, and noise reduction. The sequential forward selection (SFS) method and Fisher objective function were used as the search strategy and feature optimization. Three classifiers were tested (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbors algorithm (KNN)) to find the best performer. On the other hand, in the transfer learning (deep learning) approaches, simple ConvNet, AlexNet, VGG-19, ResNet-50, and ResNet-101 were used to train the dataset and class prediction of the seed. For the supervised model development (both conventional machine learning and deep learning), the germination test results of the samples were used where the seeds were divided into two classes: (1) normal viable seeds and (2) nonviable and abnormal viable seeds. In the conventional classification, 83.6% accuracy was obtained by LDA using 48 features. ResNet-50 performed better than other transfer learning architectures, with an 87.3% accuracy which was the highest accuracy in all classification models. The findings of this study manifested that transfer learning is a constructive strategy for classifying seeds by analyzing their morphology, where X-ray imaging can be adopted as a potential imaging technique. [ABSTRACT FROM AUTHOR]

The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89–100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration. [ABSTRACT FROM AUTHOR]

In this study, a high-sensitivity Pb( Mg 1 / 3 Nb 2 / 3 ) O 3 - PbTiO 3 (PMN-PT)-based ultrasonic transducer was developed for detecting defective pressurized water reactor (PWR) fuel rods. To apply the PMN-PT substance to nuclear power plant facilities, given the need to guarantee their robustness against radioactive materials, the effects of neutron irradiation on PMN-PT were investigated. As a result, the major piezo-electric constants of PMN-PT, such as the electrical impedance, dielectric constant, and piezo-electric charge constant, were found to vary within acceptable ranges. This means that the PMN-PT could be used as the piezo-electric material in the ultrasonic transducer for nuclear power plants. The newly developed ultrasonic transducer was simulated using a modified KLM model for the through-transmission method and fabricated under the same conditions as in the simulation. The through-transmitted waveforms of normal and defective PWR fuel rods were obtained and compared with simulated results in the time and frequency domains. The response waveforms of the newly developed ultrasonic transducer for pressurized water reactor (PWR) fuel rods showed good agreement with the simulation outcome and could clearly detect defective specimens with high sensitivity. [ABSTRACT FROM AUTHOR]