Coffee Arabica Nutrient Deficiency Detection System Using Image Processing Techniques

Abstract

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 experimental research design which involves dataset preparation, designing classification 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 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 doing more researchers by using others CNN architectures and more datasets.

Plant phenotyping relevance

コーヒー葉画像から栄養欠乏という植物状態を推定する画像処理・深層学習モデルを開発し、複数モデルで性能評価しているため、フェノタイピング手法が中心である。

Code and data availability

The paper describes a 422-image coffee leaf dataset and MobileNet/VGG16/InceptionV3 experiments, but contains no public dataset deposit, no author code/workflow URL, and no availability statement. The only URLs are the DOI and an ORCID profile, neither of which hosts the data or code.

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