Peanut maturity classification using hyperspectral imagery
| Autor: | Seung-Chul Yoon, Alina Zare, Yu-Chien Tseng, Barry L. Tillman, Diane L. Rowland, Sheng Zou |
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| Rok vydání: | 2019 |
| Předmět: |
genetic structures
010401 analytical chemistry Economic return food and beverages Soil Science Hyperspectral imaging 04 agricultural and veterinary sciences Orange (colour) 01 natural sciences 0104 chemical sciences Human assessment Horticulture Quality research Control and Systems Engineering 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Maturity assessment Cultivar Quality characteristics Agronomy and Crop Science Food Science Mathematics |
| Zdroj: | Biosystems Engineering. 188:165-177 |
| ISSN: | 1537-5110 |
| Popis: | Seed maturity in peanut (Arachis hypogaea L.) determines economic return to a producer because of its impact on seed weight (yield), and critically influences seed vigour and other quality characteristics. During seed development, the inner mesocarp layer of the pericarp (hull) transitions in colour from white to black as the seed matures. The maturity assessment process involves the removal of the exocarp of the hull and visually categorizing the mesocarp colours into varying colour classes from immature (white, yellow, orange) to mature (brown, and black). This visual colour classification is time consuming because the exocarp must be manually removed. In addition, the visual classification process involves human assessment of colours, which leads to large variability of colour classification from observer to observer. A more objective, digital imaging approach to peanut maturity is needed, optimally without the requirement of removal of the hull's exocarp. This study examined the use of a hyperspectral imaging (HSI) process to determine pod maturity with intact pericarps. The HSI method leveraged spectral differences between mature and immature pods within a classification algorithm to identify the mature and immature pods. Therefore, there is no need to remove the exocarp nor is there a need for subjective colour assessment in the proposed process. The results showed a consistent high classification accuracy using samples from different years and cultivars. In addition, the proposed method was capable of estimating a continuous-valued, pixel-level maturity value for individual peanut pods, allowing for a valuable tool that can be utilized in seed quality research. This new method solves issues of labour intensity and subjective error that all current methods of peanut maturity determination have. |
| Databáze: | OpenAIRE |
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