Expert System in Detecting Coffee Plant Diseases

Abstract

Coffee is an important commodity in the world economy. But unfortunately, productivity and quality of those commodities results are still quite low. This is caused by the disease in coffee plants. The research objective is to create an application that can help researchers or observers working in coffee plantation to diagnose diseases of coffee plants. The method used is fuzzy logic-based expert systems, and decision tree using a hierarchical classification. Knowledge about coffee, its symptoms, and its disease is extracted from human expert and then is converted into a decision tree. It will result on the fuzzy logic-based expert systems. From the experiments, accuracy calculation of the system is about 85%. Based on the accuracy, it can be concluded that this application can be a bit much to help researchers or observers of the coffee plants in diagnosing coffee plants diseases earlier. 

Coffee Leaf Disease Recognition Using Gist Feature

“…Each component of the meta-architectures is combined with deep feature extractors like ResNet or VGG net. Expert system based on fuzzy logic and decision tree is used by Suhartono [4]. Symptoms and disease information is gathered from a human expert.…”

Deteksi Penyakit Brown Eye Spot pada Daun Kopi Menggunakan Metode Euclidean Distance dan Hough Transform

“…Jika lingkaran terdeteksi dapat diketahui jika daun terjangkit penyakit brown eye spot. Seperti penelitian yang dilakukan oleh Suhartono et al yang menggunakan Fuzzy Logic sebagai salah expert system untuk mendeteksi brown eye [9]. Disamping itu penelitian yang dilakukan Hung et al [10] yang juga melakukan penelitian yang sama hanya saja menggunakan metode yang berbeda, yaitu discriminant analysis.Oleh karena itu penelitian ini akan menggunakan metode lain untuk mengetahui tingkat akurasi yang baik.…”

Detección de enfermedades en el sector agrícola utilizando Inteligencia Artificial

“…Fuzzy logic Se basa en reglas heurísticas, se usa para procesos altamente no lineales [5]. Fácil implementación.…”

“…Derwin Suhartono et al (2013) utilizaron un sistema de lógica difusa y árboles de decisión y con la ayuda de un experto humano, pudieron hacer un reconocimiento de las enfermedades del café, donde obtuvieron las características de los sín tomas que se presentan en la planta y así poder hacer su árbol de decisión. Los resultados que obtuvieron están dentro del 85% de exactitud.…”