Development of Artificial Intelligence Climate Chamber and Experimental Study on Crop Phenotype Acquisition

Background: The deficiencies of traditional artificial climate chambers in phenotypic collection and analysis were improved to achieve the high-throughput acquisition of crop phenotypes during the growth period. This paper has developed an artificial intelligence climate cabin with functions of crop cultivation management and phenotype acquisition during the whole growth period. This research also established an environmental control system, a crop phenotype monitoring system and a crop phenotype acquisition system with environmental parameter adjustment and crop image collection. Phenotypic feature extraction and other functions were carried out in the cultivation experiment, and phenotype acquisition of wheat was performed under different nitrogen fertiliser application rates. Comparison and analyses were performed by the systematic and manual measurement values of crop phenotype characteristics, and the acquisition of wheat table was evaluated based on artificial intelligence climate cabin. The goodness of fit of the model was used to classify data.Results: During the different growth periods of wheat, the correlation analysis between the systematic and manual measurement values of its leaf area, plant height and canopy temperature showed that the obtained correlation coefficient r was greater than 1, and the fitting determination coefficient R2 was greater than 0.7156, with errors. The coefficient root mean square error was less than 2.42, indicating that the two were positively correlated, and their correlation was excellent. Conclusion: The results verified the feasibility and applicability of the artificial intelligence climate cabin to study the phenotypic characteristics of crops.

Sports Action Recognition Based on Image Processing Technology and Analysis of the Development of Sports Industry Pattern

The current era is an information age, and society is turning to the information age. The image processing technology is also widely used in various fields, and the technology of sports action recognition based on image processing technology can also be said to be appropriate. This article uses a spatial visual feature analysis algorithm to implement it. To implement this algorithm, a series of work such as image collection, feature extraction, and action recognition must be completed first and then implemented through texture functions and other related functions. This algorithm can be used to complete the image-based sports action recognition technology at the minimum time cost. This algorithm can help sportsmen better complete training and standardize movements to a certain extent. As for the development of China’s current sports industry structure, it is also steadily improving. The people’s love for sports is getting stronger and stronger, which also makes the development of China’s sports industry still benefit a lot.

Large-Area Full-Coverage Remote Sensing Image Collection Filtering Algorithm for Individual Demands

Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks for spatial statistical analysis of a whole city. Therefore, people often artificially find multiple images with complementary regions in an urban area on the premise of meeting the basic requirements for resolution, cloudiness, and timeliness. However, with the rapid increase of remote sensing satellites and data in recent years, time-consuming and low performance manual filter results have become more and more unacceptable. Therefore, the issue of efficiently and automatically selecting an optimal image collection from massive image data to meet individual demands of whole urban observation has become an urgent problem. To solve this problem, this paper proposes a large-area full-coverage remote sensing image collection filtering algorithm for individual demands (LFCF-ID). This algorithm achieves a new image filtering mode and solves the difficult problem of selecting a full-coverage remote sensing image collection from a vast amount of data. Additionally, this is the first study to achieve full-coverage image filtering that considers user preferences concerning spatial resolution, timeliness, and cloud percentage. The algorithm first quantitatively models demand indicators, such as cloudiness, timeliness, resolution, and coverage, and then coarsely filters the image collection according to the ranking of model scores to meet the different needs of different users for images. Then, relying on map gridding, the image collection is genetically optimized for individuals using a genetic algorithm (GA), which can quickly remove redundant images from the image collection to produce the final filtering result according to the fitness score. The proposed method is compared with manual filtering and greedy retrieval to verify its computing speed and filtering effect. The experiments show that the proposed method has great speed advantages over traditional methods and exceeds the results of manual filtering in terms of filtering effect.

Non-dominated sorting based multi-page photo collage

The development of social networking services (SNSs) revealed a surge in image sharing. The sharing mode of multi-page photo collage (MPC), which posts several image collages at a time, can often be observed on many social network platforms, which enables uploading images and arrangement in a logical order. This study focuses on the construction of MPC for an image collection and its formulation as an issue of joint optimization, which involves not only the arrangement in a single collage but also the arrangement among different collages. Novel balance-aware measurements, which merge graphic features and psychological achievements, are introduced. Non-dominated sorting genetic algorithm is adopted to optimize the MPC guided by the measurements. Experiments demonstrate that the proposed method can lead to diverse, visually pleasant, and logically clear MPC results, which are comparable to manually designed MPC results.

Defect segmentation for multi-illumination quality control systems

Thanks to recent advancements in image processing and deep learning techniques, visual surface inspection in production lines has become an automated process as long as all the defects are visible in a single or a few images. However, it is often necessary to inspect parts under many different illumination conditions to capture all the defects. Training deep networks to perform this task requires large quantities of annotated data, which are rarely available and cumbersome to obtain. To alleviate this problem, we devised an original augmentation approach that, given a small image collection, generates rotated versions of the images while preserving illumination effects, something that random rotations cannot do. We introduce three real multi-illumination datasets, on which we demonstrate the effectiveness of our illumination preserving rotation approach. Training deep neural architectures with our approach delivers a performance increase of up to 51% in terms of AuPRC score over using standard rotations to perform data augmentation.

Aldrovandi, truthfully drawing naturalia, and local context

This essay focuses on the 16th -century Bolognese naturalist and collector Ulisse Aldrovandi (1522-1605) and his enormous image collection of naturalia. Do these images present a specifically Bolognese form of visual natural science, and was his visual format of truthfulness new at the time? Did Local visual culture leave clear marks on Aldrovandi's image collection? On cover:ANNIBALE CARRACCI (BOLOGNA 1560 - ROME 1609), An Allegory of Truth and Time c. 1584-1585.Oil on canvas | 130,0 x 169,6 cm. (support, canvas/panel/str external) | RCIN 404770Royal Collection Trust / © Her Majesty Queen Elizabeth II 2021.

Large Scale Infrared-Based Remote Sensing of Turbulence Metrics in Surface Waters: Going Beyond Mean Flow

In recent years field-scale applications of image-based velocimetry methods, often referred to as large scale particle image velocimetry (LSPIV), have been increasingly deployed. These velocimetry measurements have several advantages—they allow high resolution, non-contact measurement of surface velocity over a large two dimensional area, from which the bulk flow can be inferred. However, visiblelight LSPIV methods can have significant limitations. The water surface often lacks natural features that can be tracked in the visible and generally requires seeding with tracer particles, which creates concerns regarding the fidelity with which tracer particles track the flow, and introduces challenges in achieving sufficient and uniform seeding density, in particular in regions with appreciable velocity accelerations such as turbulence. In LSPIV, image collection is generally limited to daylight hours, and can suffer from non-uniformity of illumination across the camera’s field of view. Due to these issues LSPIV often requires spatio-temporal averaging, and as a result is generally able to extracting the mean, but not the instantaneous, velocity field, and hence is often not a suitable tool for calculating turbulence metrics of the flow.