Deep Learning Modeling for Potato Breed Recognition

Abstract

Potatoes are one of the world's most popular and economically important crops. For many uses in agriculture, breeding, and trading, accurate recognition of potato breeds is important. In recent years, deep learning algorithms have become effective tools for breed recognition tasks using pictures, which inspires researchers to explore their potential for recognizing potato breeds. The paper presents extensive research on the application of deep learning for potato breed recognition. The recognition of potatoes has been effectively performed using the five state-of-the-art deep learning models VGG16, ResNet50, Mobile-Net, Inception-v3, and a customized CNN. These models have been modeled to differentiate between several potato breeds based on their unique visual characteristics, such as size, shape, color, texture, and skin pattern, by being trained on images of various potato breeds. The performance of each of the deep learning models is evaluated through thorough evaluation and testing. Among the models, the customized CNN model gives the best accuracy. The customized CNN model's accuracy is 94.84%. We do not just evaluate the accuracy but rather some other indicative metrics, such as F1-score, recall, and precision, too.