Detection of Tomato Leaf Miner Using Deep Neural Network.

As a result of climate change and global warming, plant diseases and pests are drawing attention because they are dispersing more quickly than ever before. The tomato leaf miner destroys the growth structure of the tomato, resulting in 80 to 100 percent tomato loss. Despite extensive efforts to prevent its spread, the tomato leaf miner can be found on most continents. To protect tomatoes from the tomato leaf miner, inspections must be performed on a regular basis throughout the tomato life cycle. To find a better deep neural network (DNN) approach for detecting tomato leaf miner, we investigated two DNN models for classification and segmentation. The same RGB images of tomato leaves captured from real-world agricultural sites were used to train the two DNN models. Precision, recall, and F1-score were used to compare the performance of two DNN models. In terms of diagnosing the tomato leaf miner, the DNN model for segmentation outperformed the DNN model for classification, with higher precision, recall, and F1-score values. Furthermore, there were no false negative cases in the prediction of the DNN model for segmentation, indicating that it is adequate for detecting plant diseases and pests.

Prediction of Driver's Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques.

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver's intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver's intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver's intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver's intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

Acetoacetate protects neuronal cells from oxidative glutamate toxicity.

Glutamate cytotoxicity contributes to neuronal degeneration in many central nervous system (CNS) diseases, such as epilepsy and ischemia. We previously reported that a high-fat and low-carbohydrate diet, the ketogenic diet (KD), protects against kainic acid-induced hippocampal cell death in mice. We hypothesized based on these findings that ketosis resulting from KD might inhibit glutamate cytotoxicity, resulting in inhibition of hippocampal neuronal cell death. Therefore, we investigated the role of ketone bodies [acetoacetate (AA) and beta-hydroxybutyrate (beta-OHB)] both in a mouse hippocampal cell line (HT22) and in rat primary hippocampal neurons. As a result, we found that pretreatment with 5 mM lithium AA and 4 mM Na beta-OHB protected the HT22 hippocampal cell line and primary hippocampal neuronal culture against 5 mM glutamate toxicity and that up to 2 hr of pretreatment with 5 mM AA had a protective effect against 5 mM glutamate toxicity in the HT22 cell line. Pretreatment with 5 mM AA decreased ROS production of HT22 cell line at 2 and 8 hr exposure of glutamate, and it decreased the appearance of annexin V-positive HT22 cells, which are indicative of an early stage of apoptosis, and propidium iodide-positive HT22 cells, which are indicative of necrosis.