Web-based CNN Application for Arabica Coffee Leaf Disease Prediction
in Smart Agriculture

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction
in Smart Agriculture

arabica coffee, convolutional neural networks, image processing, leaf disease, machine learning

n the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in
eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional
neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's
help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and
pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the
neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research
presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research,
5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy
of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation,
and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed
by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate
than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was
chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf
diseases, as shown by the system's implementation outcomes

Yazid Aufar1
, Muhammad Helmy Abdillah2
, Jiki Romadoni3

Politeknik Hasnur

03-02-2023

Fajar bagus W

PDF

Indonesia

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