Classification of lentil seed vigor based on seedling image analysis techniques and interactive machine learning

Abstract

Abstract: The search for techniques that allow for the rapid and accurate assessment of seed vigor, such as the Seedling Analysis System (SAPL®) and ILASTIK®, can be promising alternatives for seedling image analysis. The objective of this work was to classify the vigor of lentil seeds using seedling image analysis techniques and interactive machine learning. Seeds from seven lots were characterized for physiological potential through germination and vigor tests. For computerized seedling analysis, the seeds were subjected to seedling growth tests at 20 °C for three, four, five, and ten days, and then photographed using a digital camera. The images were processed using SAPL® software, yielding values for total length, root length, shoot length, and vigor, growth, and uniformity indices. ILASTIK® provided data on the percentage of vigorous seedlings, non-vigorous seedlings, and dead seeds. The total length of seedlings, root length, shoot length, and vigor indices determined at 4 days of germination by SAPL® allowed for the classification of lots in terms of vigor. Data obtained by ILASTIK® at 4 days of germination, used in machine learning studies, enable the development of models with high accuracy for seed vigor assessment.