Prowess of Meta-heuristics to Distinguish Potatoes

Abstract In general, the collaboration between technology and agriculture is getting stronger. Agriculture covers many crops, including vegetables and fruit types which are grown under diverse conditions. Climate and soil type are major factors during the growing process of potato. Another factor, the tuber itself, also plays a crucial role in determining characteristics of potato growth. It is therefore beneficial to distinguish different types of tubers, especially in the supply chain. Closer inspection of tubers could serve as an effective method for analyzing the elements and minerals within the potato once tubers have been correctly separated. Using machine learning in this field significantly contributes to obtaining more advanced conclusions. The Visual Geometry Group made the VGG-19 model, which was used to sort different cultivars of potatoes. Transfer learning was used by stopping the last layer of the neural network. Going further, five types of meta-heuristic methods (GA, PSO, ABC, ALO, and GWO) were used to get the best hyperparameters that yielded better results than without using meta-heuristics. Artificial Bee Colony (ABC) surpasses the other methods as well as the standard approach by reaching 90% accuracy, which was better than the standard by 23% for six cultivars of potatoes on the test set while acquiring the best values.