An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning

Abstract

In recent years, deep learning methods have been introduced for segmentation and classification of leaf lesions caused by pests and pathogens. Among the commonly used approaches, convolutional neural networks have provided results with high accuracy. The purpose of this work is to present an effective and practical system capable of segmenting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural networks. The proposed approach consists of two stages: a semantic segmentation stage with severity calculation and a symptom lesion classification stage. Each stage was tested separately, highlighting the positive and negative points of each one. We obtained very good results for the severity estimation, suggesting that the model can estimate severity values very close to the real values. For the biotic stress classification, the accuracy rates were greater than 97%. Due to the promising results obtained, an App for Android platform was developed and implemented, consisting of semantic segmentation and severity calculation, as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.

Keywords

• Convolutional neural networks
• Semantic segmentation
• Coffee leaf
• Pests and diseases
• App. development