Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things

The accurate judgement of fruit maturity is significant for modern agriculture. At present, few scholars have monitored and recognized fruit maturity based on the Internet of things (IoT) and image recognition technology. Therefore, this paper explores the image recognition of fruit maturity in the context of agricultural Internet of things (IoT). Firstly, the single shot multi-box detection (SSD) algorithm was improved for fruit recognition and positioning, and used to determine the size and position the fruits to be recognized. Next, an image fusion algorithm was designed based on improved Laplacian pyramid, which effectively compresses the large fruit monitoring images shot in the same scene. The proposed algorithm was proved feasible and effective through experiments.

An Improved Version of the Generalized Laplacian Pyramid Algorithm for Pansharpening

The spatial resolution of multispectral data can be synthetically improved by exploiting the spatial content of a companion panchromatic image. This process, named pansharpening, is widely employed by data providers to augment the quality of images made available for many applications. The huge demand requires the utilization of efficient fusion algorithms that do not require specific training phases, but rather exploit physical considerations to combine the available data. For this reason, classical model-based approaches are still widely used in practice. We created and assessed a method for improving a widespread approach, based on the generalized Laplacian pyramid decomposition, by combining two different cost-effective upgrades: the estimation of the detail-extraction filter from data and the utilization of an improved injection scheme based on multilinear regression. The proposed method was compared with several existing efficient pansharpening algorithms, employing the most credited performance evaluation protocols. The capability of achieving optimal results in very different scenarios was demonstrated by employing data acquired by the IKONOS and WorldView-3 satellites.

THE ESTIMATION OF RESPIRATION RATE BASED ON THE AMPLIFICATION OF RESPIRATION MOTION IN VIDEO

Standard instrument for the clinical diagnosis of sleep apnea is large and based on invasive method, which is not comfortable and not suitable for daily inspection. A video-based measurement method for the respiration rate (RR) is therefore proposed, which is meaningful to the home diagnosis of sleep apnea. We proposed a novel method for the visualization and calculation of RR from a video containing a sleeping person. The video was decomposed by spatio-temporal Laplacian pyramid method into multiresolution image sequences, which were filtered by an infinite-impulse-response bandpass filter to extract the respiration movement in the video. The respiration movement was amplified, and fused into the original video. On the other hand, the signal intensity of the filtering results was compared between layers of Laplacian pyramid to identify the layer with the strongest movement caused by respiration. A morphological calculation was conducted on the image reshaped from the filtered results in this layer, to find the region of interest (ROI) with most significant movement of respiration. The image intensity in the ROI was spatially averaged into a one-dimensional signal, of which the frequency domain was analyzed to obtain RR. The ROI and the calculation results for RR were visualized on the video with enhanced respiration movement. Ten videos lasting 30–60[Formula: see text]s were recorded by a general webcam. The respiration movement of the subject was successfully extracted and amplified, no matter the posture was supine or side lying. The thoracic and abdominal parts were generally identified as ROI in all postures. RR was calculated by the frequency domain analysis for the averaged image intensity in ROI with the error no more than 1 time per minute, and further, as well as ROI, was fused into the amplified video. The region of respiration movement and RR is calculated by the noncontact method, and well visualized in a video. The method provides a novel screening tool for the population suspected of sleep apnea, and is meaningful to the home diagnosis of sleep illness.