Coffee Leaf Disease Recognition Using Gist Feature

Abstract

Coffee leaf disease recognition is important as its quality can be affected by the disease like -rust. This paper presents a coffee leaf disease recognition system with the help of gist feature. This research can help coffee producers in diagnosis of coffee plants in initial stage. Rocole coffee leaf dataset is considered in this study. Input image is preprocessed first. Resize and filtering is used as pre-processing work. Gist feature is extracted from pre-processed image. Extracted features are trained with machine learning algorithm. In testing phase, features are extracted and tested with trained ML model. Simulation is done with 10 fold cross validation. Different ML models are used and selected the best among them based on performance. SVM achieved overall 99.8% accuracy in recognizing coffee leaf disease.

A novel deep learning architecture for disease classification in Arabica coffee plants

“…A disease recognition system for Rocole coffee leaves was presented by Chowdhury using gist features 19 . It was inferred that gist descriptors of 128 and 256 feature vectors have performed considerably well.…”

“…A disease recognition system for Rocole coffee leaves was presented by Chowdhury using gist features. 19 It was inferred that gist descriptors of 128 and 256 feature vectors have performed considerably well. Extracted features were then fit in various machine learning models, out of which SVM obtained the best results.…”

Comparação de desempenho entre Random Forest e MobileNetV2 para classificação de imagens de folhas de café

“…O artigo de [Chowdhury 2021] presenta uma abordagem utilizando características globais de e 640 imagens do banco de dados (RoCoLe). As características foram obtidas por meio da técnica GIST (Global Image Structure), que se concentra na extrac ¸ão de informac ¸ões globais das imagens, incluindo distribuic ¸ão de texturas, bordas e orientac ¸ões, sem considerar informac ¸ões locais.…”