Abstract:
This study mainly focused on the detection of Coffee Arabica nutrient deficiency
by using image processing techniques. Coffee nutrition deficiency techniques are very
traditional and time taking which means the agronomists simply detect deficiencies
by observing the leaves of the coffee and decide by guessing. The study employed ex perimental research design which involves dataset preparation, designing classifica tion model and evaluation. In addition, Python programming languages were used.
The researcher has 422 total nutritional deficient Coffee plant leaves image data set,
from this data first the researcher split 20 percent for testing which is 84 images and
338 training image data, and further from the remaining training data, the researcher
again split20 percent validation data which is 10 images. The three pre-trained deep
learning models were used to evaluate the experiments. The evaluation of the system
indicated the performance of Mobile Net (0.9882), VGG16 Net (0.6471) and Incep tion_V3 (0.8095). Therefore, testing and training value of Mobile Net model was more
accurate than the rest of two models. Finally, the prototype for detection of Coffee nu trient deficiency developed by using Mobile Net deep learning model. For the feature
the researchers suggest doing more researchers by using others CNN architectures
and more datasets.
