An AI-Horticulture Monitoring and Prediction System with Automatic Object Counting

Estimating density maps and counting the number of objects of interest from images has a wide range of applications, such as crowd counting, traffic monitoring, cell microscopy in biomedical imaging, plant counting in agronomy, as well as environmental survey. Manual counting is a labor-intensive and time-consuming process. Over the past few years, the topic of automatic object counting by computers has been actively evolving from the classic machine learning methods based on handcrafted image features to end-to-end deep learning methods using data-driven feature engineering, for example by Convolutional Neural Networks (CNNs). In our research, we focus on the task of counting plants for large-scale nursery farms to build an AI-horticulture monitoring and prediction system using unmanned aerial vehicle (UAV) images. The common challenges of automatic object counting as other computer vision tasks are scenario difference, object occlusion, scale variation of views, non-uniform distribution, and perspective difference. For an AI-horticulture monitoring and prediction system for large-scale analysis, the plant species various a lot, so that the image features are different based on different appearance of species. In order to solve these complex problems, the deep convolutional neural network-based approaches are proposed. Our method uses the density map as the ground truth to train the modified classic deep neural networks for object counting regression. Experiments are conducted comparing our proposed models with the state-of-the-art object counting and density estimation approaches. The results demonstrate that our proposed counting model outperforms state-of-the-art approaches by achieving the best counting performance with a mean absolute error of 1.93 and a mean square error of 2.68 on our horticulture nursery plant dataset

Influence of Personality Characteristics and Psychological Intervention on Treatment Satisfaction of Juvenile Orthodontic Patients

Orthodontics is the correction and treatment of malocclusion deformity caused by a variety of reasons. Malocclusion malformation has a direct impact on people’s facial features, while likely to cause some diseases involving the mouth in the long-term life. For adolescents, malocclusion has a great physical and mental impact. This article first have a simple overview of malocclusion deformity and orthodontic treatment, analysis of youth physical and mental development characteristics and adolescent personality traits. Through the way of completely random sampling, eighty teenage orthodontic patients can be divided into two groups, respectively as the control group and psychological intervention group. Though survey assessment after several stages treatment, explore impact on the psychological intervention in patients with juvenile orthodontic treatment satisfaction degree

Recent examples of α-ketoglutarate-dependent mononuclear non-haem iron enzymes in natural product biosyntheses

Covering: up to 2018 α-Ketoglutarate (αKG, also known as 2-oxoglutarate)-dependent mononuclear non-haem iron (αKG-NHFe) enzymes catalyze a wide range of biochemical reactions, including hydroxylation, ring fragmentation, C-C bond cleavage, epimerization, desaturation, endoperoxidation and heterocycle formation. These enzymes utilize iron(ii) as the metallo-cofactor and αKG as the co-substrate. Herein, we summarize several novel αKG-NHFe enzymes involved in natural product biosyntheses discovered in recent years, including halogenation reactions, amino acid modifications and tailoring reactions in the biosynthesis of terpenes, lipids, fatty acids and phosphonates. We also conducted a survey of the currently available structures of αKG-NHFe enzymes, in which αKG binds to the metallo-centre bidentately through either a proximal- or distal-type binding mode. Future structure-function and structure-reactivity relationship investigations will provide crucial information regarding how activities in this large class of enzymes have been fine-tuned in nature.R01 GM093903 - NIGMS NIH HHSAccepted manuscrip