## 1 Introduction  
Pole sitao (_Vigna unquiculata_ ([PERSON]), a member of the Leguminosae family, is one of the most cultivated crops in the Philippines. It is also popularly known as asparagus bean, Chinese long bean, garter bean, snake bean, or yard long bean. It can be served steamed, sauteed, buttered, or cooked together with other vegetables [1]. The quality of the pole sitao seed is one of the main factors that determine the harvests' quality and quantity. In choosing the best seed to plant, it should have a good germination rate to maximize the yield and achieve the desired quality of the harvested vegetable. However, the manual inspection and classification of seeds for germination tests can be a time-consuming, inconsistent, and laborious task for the inspector [2]. Seeds subjected to germination tests can be classified into three categories: normal, abnormal, and ungerminated [3]. Seeds under the normal classification have well-developed and complete germinate parts. Seedsunder the abnormal classification have stunted or missing germinate parts. Lastly, seeds under the unperminated classification show no sign of germination. With the amount of detail required for inspection, manual inspection and classification are time-consuming, especially if there are many seeds to be inspected and classified. Given the prescribed observation days of seeds, the inspection and classification should be finished within the prescribed time frame. Also, the inspector can have an inconsistent judgment, especially for inexperienced inspectors, and the judgment varies from person to person inspecting the seeds. Moreover, it can be laborious and cause eye strain since inspecting and classifying seeds include a series of steps from inspecting them one by one to classifying them based on the set criteria. A possible tool that can be used to address the difficulty of manual classification is to automate the process and use machine vision and machine learning. Machine vision helps to automate the process of classifying seeds in the germination test. Similarly, this technique combined with machine learning technology can be used in performing the classification for germination tests, which is also becoming a powerful tool for this process. Machine learning is a state-of-the-art technology that is used to mimic the human brain for independent decision-making of the machine. It uses models that act as a brain that can be trained to learn the features and characteristics of the input dataset. In modern machine learning, the technology uses neural network-based models that can process data in a short amount of time [4]. By utilizing these technologies, it is possible to assess and classify the quality of seeds based on the set standards with relatively higher speed and accuracy compared to conventional methods. Some studies have been conducted to solve this problem by automating the above-mentioned process. In the study by [PERSON] and [PERSON] [5] on rice seeds, the authors developed MATLAB-based software called _Rice Seed Germination Analysis_. The software claims to classify germinated rice seeds from unperminated seeds. The authors trained the software using four algorithms and models: _Euclidean Distance_ (ED), _Rule-Based System_ (RBS), _Fuzzy Logic_ (FL), and _Artificial Neural Network_ (ANN). A total of 600 images of rice seeds were used to train the models, and 120 images were used for testing. The software generated testing accuracies of 87.50%, 100%, 100%, and 100% for ED, RBS, FL, and ANN, respectively. Another study on automated seed germination tests was conducted by [PERSON] et al. [6] for various crops. They used machine learning models to classify the germinated seeds of _Zea mays_ (maize), _Secale cereale_ (rye), and _Pennisetum glaucum_ (pear millet) into two classifications: germinated and non-serminated. A total of around 24,000 seeds for the three species were used for the study, 2449 of which were used as testing data, while the remaining were used as a training dataset.