This study presents a novel method to apply the RGB-D (Red Green Blue–Depth) sensors and fuse aligned RGB and NIR images with deep convolutional neural networks (CNN) for fruit detection. It aims to build a more accurate, faster, and more reliable fruit detection system, which is a vital element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). A common Faster R-CNN network VGG16 was adopted through transfer learning, for the task of kiwifruit detection using imagery obtained from two modalities: RGB (red, green, blue) and Near-Infrared (NIR) images. Kinect v2 was used to take a bottom view of the kiwifruit canopy’s NIR and RGB images. The NIR (1 channel) and RGB images (3 channels) were aligned and arranged side by side into a 6-channel image. The input layer of the VGG16 was modified to receive the 6-channel image. Two different fusion methods were used to extract features: Image-Fusion (fusion of the RGB and NIR images on input layer) and Feature-Fusion (fusion of feature maps of two VGG16 networks where the RGB and NIR images were input respectively). The improved networks were trained end-to-end using back-propagation and stochastic gradient descent techniques and compared to original VGG16 networks with RGB and NIR image input only. Results showed that the average precision (APs) of the original VGG16 with RGB and NIR image input only were 88.4% and 89.2% respectively, the 6-channel VGG16 using the Feature-Fusion method reached 90.5%, while that using the Image-Fusion method reached the highest AP of 90.7% and the fastest detection speed of 0.134 s/image. The results indicated that the proposed kiwifruit detection approach shows a potential for better fruit detection.

Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R²c) for the training set and (R²p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.

A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA

Lung sounds, as an important physiological signal of human body, play a crucial role in the diagnosis and monitoring of respiratory diseases. In recent years, deep learning-based lung sound intelligent recognition technology has made significant progress in the field of medical auxiliary diagnosis, particularly with the deployment of deep learning models on edge devices to achieve local inference and low-latency diagnosis. This has become a key direction in the development of lung sound recognition systems. This paper systematically reviews the fundamental processes of lung sound recognition and analysis, focusing on the construction of lung sound classification models, model lightweight design, and the latest research progress in deploying these models on embedded systems, FPGA, and other hardware platforms. Additionally, the paper looks forward to the application prospects and development trends of this technology in the field of intelligent lung sound recognition. In particular, deployment pathways grounded in hardware–software co-design on embedded and edge platforms are poised to further advance health monitoring systems. This paper provides a comprehensive reference for understanding the key aspects of lung sound recognition technology, from algorithm research to hardware implementation.