Eucalyptus Trees Nutrition Deficiency Detection Using Deep Learning Techniques

Abstract

Eucalyptus is one of the most widely cultivated tree species in Ethiopia and as a major source of fuel wood and construction material. A few researchers have attempted to do their research in other countries try to solve Eucalyptus tree nutrient deficiency by identifying the leaf part of the tree. According to this idea many nutrition deficiencies that affect Eucalyptus tree vegetation growth in Ethiopia. In this article, a model was developed using the deep-learning techniques, that developing Eucalypts trees nutrition deficiency detection. The study employs four distinct architectures, using one of a cloud software tool that known as "Google colab" or "Google collaborator"; MobileNet, ResNet50, DenseNet, and InceptionV3 to analyze and classify nutrient shortages from Eucalyptus tree leaf images. We collected 843 images, from its dataset 80% of the data are utilized for the training set, or a total of 674 images for all class; the remaining 10% are used to test the model and 10% images are used for the validation set for each class, MobileNet received a more hopeful result scored based on the report of classification parameters between all models. The Eucalyptus nutritional deficiency was correctly identified based on the collected images. A thorough investigation of the above mentioned models during the process of differentiating between the nutrient-deficient and healthy managed the following nutrients' Boron (B), Calcium (Ca), Iron (Fe), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Potassium (K), and Healthy on images of the leaves. Additionally, the model's data performance could be improved. Better forest management will result from this research's difficult way of monitoring Eucalyptus tree health, which will benefit precision agriculture production.