Abstract:
This study mainly focused on the detection of Coffee Arabica nutrient deficiency by using
image processing techniques. There are problems on Coffee productivity because of
Coffee nutrition deficiency. 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 experimental
research design which involves dataset preparation, designing classification model and
evaluation. Experimentation and image processing steps are followed with: image
acquisition, image preprocessing (image filtering and attribute selection), image analysis
(segmentation, feature extraction and classification), and image understanding for raw
qualifying and image scaling. 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 Inception_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 nutrient deficiency developed by using Mobile Net deep learning
model. For the feature the researchers suggest performing deeper researchers for CNN
and image processing with regards to coffee. Also, this research can be improved in
terms of portability and innovative collaboration with another platform technology.
