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Robotic fruit harvesting has great potential to revolutionize agriculture, but detecting cherry tomatoes in farming environments still faces challenges in accuracy and efficiency. To overcome the shortcomings of existing cherry tomato detection methods for harvesting, this study introduces a deep-learning-based cherry tomato detection scheme for robotic harvesting in greenhouses using multimodal RGB-D perception and an improved YOLOv7-tiny Cherry Tomato Detection (YOLOv7-tiny-CTD) network, which has been modified from the original YOLOv7-tiny by eliminating the "Objectness" output layer, introducing a new "Classness" method for the prediction box, and incorporating a new hybrid non-maximum suppression. Acquired RGB-D images undergo preprocessing such as color space transformation, point cloud normal vector angle computation, and multimodal regions of interest segmentation before being fed into the YOLOv7-tiny-CTD. The proposed method was tested using an AGV-based robot in a greenhouse cherry tomato farming facility. The results indicate that the multimodal perception and deep learning method improves detection precision and accuracy over existing methods while running in real time, and the robot achieved over 80% successful picking rates in two-trial mode in the greenhouse farm, showing promising potential for practical harvesting applications. [ABSTRACT FROM AUTHOR]

The concept of Image Phase Congruency (IPC) is deeply rooted in the way the human visual system interprets and processes spatial frequency information. It plays an important role in visual perception, influencing our capacity to identify objects, recognize textures, and decipher spatial relationships in our environments. IPC is robust to changes in lighting, contrast, and other variables that might modify the amplitude of light waves yet leave their relative phase unchanged. This characteristic is vital for perceptual tasks as it ensures the consistent detection of features regardless of fluctuations in illumination or other environmental factors. It can also impact cognitive and emotional responses; cohesive phase information across elements fosters a perception of unity or harmony, while inconsistencies can engender a sense of discord or tension. In this survey, we begin by examining the evidence from biological vision studies suggesting that IPC is employed by the human perceptual system. We proceed to outline the typical mathematical representation and different computational approaches to IPC. We then summarize the extensive applications of IPC in computer vision, including denoise, image quality assessment, feature detection and description, image segmentation, image registration, image fusion, and object detection, among other uses, and illustrate its advantages with a number of examples. Finally, we discuss the current challenges associated with the practical applications of IPC and potential avenues for enhancement. [ABSTRACT FROM AUTHOR]

Infrared perfect absorption has been widely investigated due to its potential applications in photodetectors, photovoltaics and medical diagnostics. In this report, we demonstrate that at particular infrared frequencies, a simple planar structure made up of graphene-hexagonal Boron Nitride (hBN) hyper-structure is able to nearly perfectly absorb incident light irrespective of its polarization (Transverse-Magnetic TM, or Transverse-Electric TE). By using this interferenceless technique, the hyper-structure achieves nearly zero reflectance at a wide range of angles in a narrow frequency band. We analytically predict the condition of achieving such an important feature of perfect absorption for both TM and TE polarizations. Interestingly, the infrared perfect absorption can be redshifted by increasing the thickness of the hBN layers and blueshifted by increasing the graphene’s chemical potential. Such flexible control of infrared perfect absorption offers a new tool for controlling electromagnetic waves and has potential applications in photodetection and other light control applications. [ABSTRACT FROM AUTHOR]

Rationale: Primary ovarian leiomyoma is a rare benign tumor. The exact histological origin and pathogenesis of primary ovarian leiomyoma are still unclear, while its preoperative imaging diagnosis is often challenging and prone to misdiagnosis. The study aims to elucidate the diagnosis of primary ovarian leiomyoma and to distinguish it from fibroma., Patient Concerns: A 34-year-old female was admitted to the hospital with complaints of pelvic mass found for one year. One years ago, the patient went to a local hospital for examination due to irregular menstruation., Diagnoses: The ultrasound report of the patient showed ovarian teratoma. The postoperative pathological results showed ovarian leiomyoma and calcification., Interventions: The patient underwent laparoscopic right ovarian leiomyoma resection., Outcomes: The patient was discharged home three days after surgery. At the most recent follow-up (five months after operation) of the patients, ultrasound was performed and no abnormal echoes were suggested in the adnexal region., Lessons: In the diagnosis of primary ovarian leiomyoma, our case emphasizes the importance of microscopic features as an effective approach to distinguish it from ovarian fibroma, leiomyosarcoma, and stromal tumors. Additionally, personalized treatment should be considered based on the patient age and fertility needs., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)