The Application of Terahertz Time-Domain Spectroscopy to Identification of Potato Late Blight and Fusariosis

Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods

As a harmless detection method, terahertz has become a new trend in security detection. However, there are inherent problems such as the low quality of the images collected by terahertz equipment and the insufficient detection accuracy of dangerous goods. This work advances BiFPN at the neck of YOLOv5 of the deep learning model as a mechanism to improve low resolution. We also perform transfer learning, thereby fine-tuning the pre-training weight of the backbone for migration learning in our model. Results from experimental analysis reveal that mAP@0.5 and mAP@0.5:0.95 values witness a percentage increase of 0.2% and 1.7%, respectively, attesting to the superiority of the proposed model to YOLOv5, which is the state-of-the-art model in object detection.

Raman Spectroscopy Enables Non-Invasive Identification of Mycotoxins p. Fusarium of Winter Wheat Seeds

Identification of specific mycotoxins p. Fusarium contained in infected winter wheat seeds can be achieved by visually recognizing their distinctive phenotypic species. The visual identification (ID) of species is subjective and usually requires significant taxonomic knowledge. Methods for the determination of various types of mycotoxins of the p. Fusarium are laborious and require the use of chemical invasive research methods. In this research, we investigate the possibility of using Raman spectroscopy (RS) as a tag-free, non-invasive and non-destructive analytical method for the rapid and accurate identification of p. Fusarium. Varieties of the r. Fusarium can produce mycotoxins that directly affect the DNA, RNA and chemical structure of infected seeds. Analysis of spectra by RS methods and chemometric analysis allows the identification of healthy, infected and contaminated seeds of winter wheat with varieties of mycotoxins p. Fusarium. Raman seed analysis provides accurate identification of p. Fusarium in 96% of samples. In addition, we present data on the identification of carbohydrates, proteins, fiber and other nutrients contaminated with p. Fusarium seeds obtained using spectroscopic signatures. These results demonstrate that RS enables rapid, accurate and non-invasive screening of seed phytosanitary status.

Applications of THz Spectral Imaging in the Detection of Agricultural Products

Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.