Dragon fruit stem disease classification using deep learning techniques

Abstract

Dragon fruit is one of the highly valued economic and nutraceutical fruits produced in the tropical regions. However, the cultivation of dragon fruit is often affected by several stem diseases that if not controlled on time will reduce the output. Many existing disease diagnosis techniques involve direct visit by expert practitioners which is slow, tedious and often carries the risks of human errors. As such, the use of deep learning approaches is a viable solution towards enabling the automatic and efficient identification of dragon fruit stem diseases. This research focuses on the use of deep learning methods on the classification of stem diseases of the dragon fruit in order to improve production in the agricultural sector and thus minimize the losses. Given the set of labeled images of stems of dragon fruit, several deep learning models were compared: VGG16, VGG19, ResNet50, InceptionV3 and a CNN. The accuracy, precision, recall, and F1-score were used as measurements to evaluate the models. Out of all these, InceptionV3 yielded the highest accuracy score of 92. 36% while VGG16 achieved a score of 89. 41%. The experimental results show that these new generations of neural networks are well equipped to identify the stem diseases and classify them, thus playing a crucial role in disease.