外文摘要：The easy and accurate identification of varieties is fundamental in agriculture, especially in the olive sector, where more than 1200 olive varieties are currently known worldwide. Varietal misidentification leads to many potential problems for all actors in the sector: farmers and nursery workers may establish the incorrect variety, leading to maladaptation in the field; olive oil and table olive producers may label and sell a nonauthentic product; consumers may be misled; and breeders may commit errors during targeted crossings between different varieties. To date, the standard for varietal identification and certification consists of two methods: morphological classification and genetic analysis. The morphological classification consists of the visual pairwise comparison of different organs of the olive tree, where the most important organ is considered to be the endocarp. In contrast, different methods for genetic classification exist (RAPDs, SSR, and SNP). Both classification methods present advantages and disadvantages. Visual morphological classification requires highly specialized personnel and is prone to human error. Genetic identification methods are more accurate but incur a high cost and are difficult to implement. This paper introduces OliVaR, a novel approach to olive varietal identification. OliVaR used a teacher- student deep learning architecture to learn the defining characteristics of the endocarp of each specific olive variety and perform varietal classification. We construct what is, to the best of our knowledge, the largest olive variety dataset to date, comprising image data for 131 varieties from the Mediterranean basin. We thoroughly test OliVaR on this dataset and show that it correctly predicts olive varieties with over 86% accuracy.
外文关键词：machine learning;deep neural networks;image analysis;Olive variety recognition;Olive variety identification;Knowledge-driven learning
作者：Miho, Hristofor;Pagnotta, Giulio;De Gaspari, Fabio;Hitaj, Dorjan;Mancini, Luigi Vincenzo;Koubouris, Georgios;Godino, Gianluca;Hakan, Mehmet;Diez, Concepcion Munoz
作者单位：Univ Cordoba;Sapienza Univ Roma;Inst Olive Tree Subtrop Crops & Viticulture;Council Agr Res;Olive Res Inst
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：216
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台