ABSTRACT
An automatic coffee plant disease recognition system is required since coffee is an important commodity in the world economy and its productivity and quality are affected by diseases such as Coffee Leaf Miner, Coffee Leaf Rust, Coffee Leaf Spot and Coffee Phoma Leaf Rust. This project aims to apply computational methods to recognize main diseases in coffee leaves, with the purpose to implement an expert system to assist coffee producers in disease diagnosis during its initial stages. Since these diseases are shapeless, it inspires a texture attribute extraction approach for pattern recognition. The results were compared with a deep learning convolutional neural network applied directly to the same collection of images,
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