Agricultural products are often required for national food quality and safety in a sustainable economy. However, agricultural products are susceptible to various types of damage during harvesting, transportation, storage, and processing. Mechanical damage, freezing injury, over-maturity, and microbial spoilage can also deteriorate structure, nutritional composition and edible quality. Therefore, rapid, accurate, and non-destructive detection can be expected to detect agricultural product damage. Quality status can be evaluated for supply chain safety. Conventional non-destructive detection has been widely applied to assess the quality of agricultural products. Several limitations remain so far, including relatively high detection costs and limited penetration of optical signals. Some technologies rely mainly on volatile compounds for quality evaluation. Therefore, highly sensitive and non-destructive techniques can be expected to monitor internal tissues in agricultural products. Among them, electrochemical impedance spectroscopy (EIS) has attracted much attention due to its electrical response information from biological tissues using alternating current excitation, indicating structural changes in agricultural products at the cellular level. Thereby, a promising approach can provide for damage detection of agricultural products. This review aims to systematically integrate the detection parameters and modeling approaches of EIS for seven categories of agricultural product damage, including mechanical damage, freezing injury, heat injury, microbial spoilage, dehydration shrinkage, over-maturity, and meat spoilage. Its technical applicability and practical limitations were also clarified after evaluation. Impedance detection was also introduced, with emphasis on the selection of equivalent circuit models, electrode types, measurement frequencies, and impedance data processing. Furthermore, a systematic analysis was then made to explore the effects of different detection methods on impedance signal acquisition and model performance. The application progress of EIS was summarized under different damage scenarios. There were significant relationships among damage-induced tissue structures, electrical responses, and model construction strategies. Theoretical support was provided for applying EIS technology to the quality assessment of agricultural products. Finally, the challenges were analyzed that restrict the practical application of EIS technology, including electrode variability, individual differences among agricultural products, miniaturization of detection devices, and quality databases. Future directions were also discussed, including the standardization of electrode structures and measurement parameters, portable detection devices suitable for complex environments, and multi-source data analysis. Important directions were determined to promote the transformation of EIS technology from laboratory research to practical applications. The application scope of EIS will be further expanded to enhance impedance measurement, sensor technologies, and machine learning algorithms. Rapid, accurate, and intelligent evaluation was facilitated to standardize measurement protocols, reliable data interpretation, and large-scale quality monitoring under different agricultural products and scenarios. The findings can provide theoretical references and technical support for agricultural products and postharvest quality control in intelligent technologies.

Seeds of major Populus cultivars were collected from across China in 2024 to build the image-data bank of over 1187000 images of singular seeds for the National Forestry and Grassland Science Data Center (NFGSDC). An innovative vibration-assisted machine-vision system was built with alternating back-lit and front-lit illumination, which incorporated a flexible vibratory panel (FVP) to manipulate the multitude of seeds to minimize the occurrence of butting or overlapping, and the lighting from alternating directions to capture phenotypic features both in silhouettes and in vivid color images. To investigate how illumination directions would affect phenotyping, morphological and chromatic metrics were measured, respectively from only the common front-lit images and through the combined use with back-lit images, and applied to distinguish different cultivars and harvest-batches. Results verified that back-lit excelled for reliable segmentation for feature images and accurate morphological metrics, especially when the closeness was clearly revealed in the clustering dendrogram between Nanlin 895 and Zhonglin 46, which shared a common genetic sourcing from P. Euramericana. In contrast, front-lit images were prone to occasional segmentation defects leading to inaccurate morphological measurements due to the highly dynamic range of seed colors, which caused the clustering to lose the genetic relevance. The power of the image-dataset of alternating illuminations was further demonstrated when a decent accuracy of 0.819 yielded from the simple support-vector-machine classification while working on only the back-lit morphological measurements, and the increase to 0.856 with statistical significance if with the addition of chromatic metrics from corresponding front-lit color images, while other image characteristics had been strictly held back. The vibration-assisted alternating illumination protocol established in this work to capture delicate seed-features of Populus cultivars may also be applied to other small grains facing similar imaging challenges, laying a sturdy step-stone of high-throughput phenotyping for large-scale breeding programs and genetic studies.