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

作     者：Jose G.M.Esgario Pedro B.C.de Castro Lucas M.Tassis Renato A.Krohling 

作者机构：LABCIN-Nature Inspired ComputingFederal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil LABCIN-Nature Inspired Computingand PPGI-Graduate Program in Computer Scienceand Production Engineering DepartmentFederal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2022年第9卷第1期

页      面：38-47页

核心收录：

学科分类：12[管理学] 0907[农学-林学] 0908[农学-水产] 08[工学] 09[农学] 090401[农学-植物病理学] 090402[农学-农业昆虫与害虫防治] 0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0905[农学-畜牧学] 0707[理学-海洋科学] 081104[工学-模式识别与智能系统] 0906[农学-兽医学] 0829[工学-林业工程] 080203[工学-机械设计及理论] 0904[农学-植物保护] 0901[农学-作物学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico Google Latin America Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, (309729/2018-1) 

主　　题：Convolutional neural networks Semantic segmentation Coffee leaf Pests and diseases App.development 

摘      要：In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and *** the commonly used approaches,convolutional neural networks have provided results with high *** purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural *** proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification *** stage was tested separately,highlighting the positive and negative points of each *** obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real *** 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 imple-mented,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.