Estimating TYLCV resistance level using RGBD sensors in production greenhouse conditions  

作　　者：Dorin Shmaryahu Rotem Lev Lehman Ezri Peleg Guy Shani 

机构地区：[1]Ben Gurion University of the Negev,Department ofSofware and Information Systems Engineering,Beer Sheva,Israel [2]b Hazera Genetics,Berurim,Israel

出　　处：《Artificial Intelligence in Agriculture》2024年第4期31-42,共12页农业人工智能(英文)

基　　金：supported by the ISF fund,grants no.964/22 and 120/18;by the Helmsley Charitable Trust through the ABC fund。

摘　　要：Automated phenotyping is the task of automatically measuring plant attributes to help farmers and breeders in developing and growing strong robust plants.An automated tool for early illness detection can accelerate the process of identifying plant resistance and quickly pinpoint problematic breeding.Many such phenotyping tasks can be achieved by analyzing images from simple,low cost,RGB-D sensors.In this paper we focused on a particular case study-identifying the resistance level of tomato hybrids to the tomato yellow leaf curl virus(TYLCV)in production greenhouses.This is a difficult task,as separating between resistance levels based on images is difficult even for expert breeders.We collected a large dataset of images from an experiment containing many tomato hybrids with varying resistance levels.We used the depth information to identify the topmost part of the tomato plant.We then used deep learning models to classify the various resistance levels.For identifying plants with visual symptoms,our methods achieved an accuracy of 0.928,a precision of 0.934,and a recall of 0.95.In the multi-class case we achieved an accuracy of 0.76 in identifying the correct level,and an error of 0.278.Our methods are not particularly tailored for the specific task,and can be extended to other tasks that identify various plant diseases with visual symptoms such as ToBRFV,mildew,ToMV and others.

关 键 词：PHENOTYPING TYLCV TOMATO Deep learning RGB-D 

分 类 号：S43[农业科学—农业昆虫与害虫防治]