Yue Mu 교수

Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which is more important for long-term yield prediction. Therefore, tomato detection that can generalize well in real tomato cultivation scenes and is robust to issues such as fruit occlusion and vari

In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach t

The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch-lea

To realize autonomous navigation and intelligent management in orchards, vehicles require real-time positioning and globally consistent mapping of surroundings with sufficient information. However, the unstructured and unstable characteristics of orchards present challenges for accurate and stable localization and mapping. This study proposes a framework fusing LiDAR, visual, and inertial data by using the extended Kalman filter (EKF) to achieve real-time localization and colorful LiDAR point-cl

Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation. In recent studies, phenotyping of rice panicles during the heading-flowering stage still lacks comprehensive analysis, especially of panicle development under different nitrogen treatments. In this work, we proposed a pipeline to automatically acquire the detailed panicle traits based on time-series images by using the YOLO v5, ResNet50, and DeepSORT models. Combined with fiel

Artificial neural network (ANN) has the characteristics of self-adaptation, self-learning, parallel processing and strong nonlinear mapping ability. Compared with traditional experimental analysis modeling, ANN has obvious advantages in dealing with multivariable nonlinear complex relationships in the process of industrial catalyst design. In the face of the complex structure of catalyst, the unclear reaction mechanism and conditions, the use of neural network for small-scale experimental data a

As a prevalent neurodevelopmental disease, attention-deficit hyperactivity disorder (ADHD) impairs the learning and memory capacity, and so far, there has been no available treatment option for long-term efficacy. Alterations in gene regulation and synapse-related proteins influence learning and memory capacity; nevertheless, the regulatory mechanism of synapse-related protein synthesis is still unclear in ADHD. LncRNAs have been found participating in regulating genes in multiple disorders. For

The leaf area-to-fruit ratio (LAFR) is an important factor affecting fruit quality. Previous studies on LAFR have provided some recommendations for optimal values. However, these recommendations have been quite broad and lack effectiveness during the fruit thinning period. In this study, data on the LAFR and fruit quality of pears at 5 stages were collected by continuously girdling bearing branches throughout the entire fruit development process. Five different clustering algorithms, including K

This study aims to characterize and identify the chemical constituents in 11 parts of Forsythia suspensa by using ultra-performance liquid chromatography-quadrupole time of flight-mass spectrometry(UPLC-Q-TOF-MS) combined with a self-established chemical constituent database, including leaves, flowers, fruits, green F. suspensa, old F. suspensa, and seeds. The quality attributes and differences of different parts of F. suspensa were evaluated by principal component analysis, partial least square

The comprehensive understanding of the dormant pruning patterns in pear trees, along with the accurate identification of shoots suitable for pruning, is essential for implementing automated pruning and fruit production. Due to the complexity of tree architecture, previous descriptions of pruning strategies were qualitative summaries based on experience. In this study, we proposed a high-precision shoot extraction pipeline through point cloud alignment at different times, enabling a quantitative

Artificial neural network (ANN) has been widely researched and applied in chemical process, because of its parallel processing and excellent nonlinear mapping ability, with strong robustness and fault tolerance. By using artificial neural network to establish the model between the properties of mixture and its molecular structure, more accurate data can be predicted and obtained than those determined by experiment. This paper summarizes the development process of artificial neural network and an