Prediction of tomato seed germination from images with deep learning

Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds not only to achieve high productivity but also for economic growth. In fact, knowing in advance the germination rate of the seeds can give farmers a better idea of how much their fields will produce. The seeds assessment can be done before or after the experiment. In the after-experiment assessment, trained analysts evaluate the seed germination by counting the seeds which present radicles or leaves emanating from them. However, the counting process done by analysts is cumbersome, error-prone, and time-consuming. Hence, machine learning-based methods have been proposed for the situation in which the assessment of seeds is done after experiment, to determine whether a seed germinated or not. Assessment of the seeds done before or after the experiment via model-based approach present many advantages: it is fast, more repeatable, and more accurate. In this thesis, we will consider the situation where the assessment of seeds is performed instead before the start of the experiment. That is, the proposed model will try to predict the seeds that are going to germinate and those that are not going to germinate before they will be placed in a chamber under proper growing condition for seven days. Prediction before the experiment holds the potential to further reduce the time required to select the seeds that are going to germinate and to let only valid seeds proceed to use the germination equipment. Therefore, in this thesis, we study the performance of a model-based approach that uses modern convolutional neural networks to predict the germination of tomato seeds, that is, whether a seed will germinate or not after it will have spent some period in the controlled environment for growing purposes.