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The quality assurance of corn seeds is of utmost significance in all stages of production, storage, circulation, and breeding. However, the traditional detection method has some disadvantages, such as high labor intensity, strong subjectivity, low efficiency, cumbersome operation, etc. In view of this, it is of great significance to study more advanced detection methods. In this paper, the application of near-infrared spectroscopy and its imaging technology in the quality detection of corn seeds was reviewed. Firstly, the principles of these two technologies were introduced, and their components, data acquisition, and processing methods, as well as portability, were compared and discussed. Then, the application of these methods to the main quality of corn seeds (including variety and purity, vigor, internal components, mycotoxins, and other qualities such as frost damage, hardness, and maturity, etc.) was reviewed. Breakthroughs and innovations have been made in detection methods, spectral preprocessing methods and recognition algorithms. The significance of corn quality characteristics and the function of the applied algorithm were emphasized. Finally, the challenges and future research direction of spectral and its imaging technology was proposed, aiming to further enhance the accuracy, reliability, and practicability of the detection technology. With the rapid development of spectral and its imaging technology, the detection methods of corn quality are also advancing with the times. This is not just for corn, but more and more crops can be accurately detected by these technologies. It will become an important means of agricultural production inspection in the future. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility of identifying freezing damage on the endosperm side and embryo side of single corn seeds was studied by combining hyperspectral imaging technology and the deep convolutional neural network (DCNN) method. Firstly, hyperspectral image data of the endosperm and embryo side of three freezing-damage categories of corn seeds were collected, and the average spectra of the endosperm part and embryo part were obtained with the range of 450–979 nm. After the spectral data were pre-processed by non-pretreatment or standard normal variation (SNV) pretreatment, a support vector machine (SVM) and a DCNN model were established for freezing-damage identification. Finally, multiple evaluation indexes (including accuracy, sensitivity, specificity, and precision) were used to comprehensively evaluate the performance of the SVM and DCNN models in the whole waveband. The results showed that the DCNN model obtained better performance in accuracy, sensitivity, specificity, and accuracy. The values of each category, especially for the category-2 and category-3 testing sets of the SVM model, were lower than those of the DCNN model. The classification results of the embryo-side corn seeds were better than those of the endosperm side. The accuracy value of the testing set of the DCNN model on the embryo side was higher than 96.7%, while the accuracy value of the DCNN model on the endosperm side was lower than 93.8%. The specificity values of the SVM and DCNN models were both higher than 94%. In addition, the sensitivity and precision values of the category-2 testing set of the embryo-side DCNN model increased by at least 2.8% and 4.8%. The sensitivity value of the category-3 testing set of the DCNN model was improved by at least 8.2% and 4.4%. These results of the embryo side of the corn seed showed significant improvement in the training and testing set. This study proved that the DCNN model can accurately and quickly identify single freezing-damage corn seeds, which provided a theoretical basis for constructing an end-to-end recognition and classification model of frozen corn seeds. [ABSTRACT FROM AUTHOR]

The application of heat pump systems in agriculture, especially within livestock farms, has attracted considerable attention due to their potential for energy efficiency and improved environmental sustainability. Many studies have explored using heat pumps to optimize the indoor environments of barns. This review offers a comprehensive overview and analysis of the current applications of heat pump systems in livestock barn environmental control. Initially, it outlines the fundamental principle of heat pumps and the various types of heat pumps. Then, the technical advantages of the heat pump systems in regulating indoor temperature and humidity of livestock facilities, improving energy efficiency, and reducing environmental impacts are evaluated. Heat pump systems outperform conventional heating and cooling methods in terms of energy utilization and cost-effectiveness, and they positively contribute to reducing environmental pollution. However, some barriers obstruct the widespread adoption of heat pump systems, including policy and regulatory, economic and financial, and technological and infrastructure, as well as public perception and awareness. Future research is recommended to address these barriers. Thus, more heat pump systems in livestock farms could be extensively applied. [ABSTRACT FROM AUTHOR]