Growth monitoring of greenhouse lettuce based on a convolutional neural network  

作　　者：Lingxian Zhang Zanyu Xu Dan Xu Juncheng Ma Yingyi Chen Zetian Fu 

机构地区：[1]China Agricultural University,Beijing 100083,China [2]Key Laboratory of Agricultural Informationization Standardization,Ministry of Agriculture and Rural Affairs,Beijing,China [3]Institute of Environment and Sustainable Development in Agriculture,Chinese Academy of Agricultural Sciences,Beijing 100081,China

出　　处：《Horticulture Research》2020年第1期1161-1172,共12页园艺研究（英文）

基　　金：supported by the Beijing Leafy Vegetables Innovation Team of Modern Agro-industry Technology Research System(BAIC07-2020);the National Key Research and Development Project of Shandong(2017CXGC0201).

摘　　要：Growth-related traits,such as aboveground biomass and leaf area,are critical indicators to characterize the growth of greenhouse lettuce.Currently,nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features.In this study,a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network(CNN).Taking lettuce images as the input,a CNN model was trained to learn the relationship between images and the corresponding growth-related traits,i.e.,leaf fresh weight(LFW),leaf dry weight(LDW),and leaf area(LA).To compare the results of the CNN model,widely adopted methods were also used.The results showed that the values estimated by CNN had good agreement with the actual measurements,with R^(2) values of 0.8938,0.8910,and 0.9156 and normalized root mean square error(NRMSE)values of 26.00,22.07,and 19.94%,outperforming the compared methods for all three growth-related traits.The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno.Generalization tests were conducted by using images of Tiberius from another growing season.The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits,with R2 values of 0.9277,0.9126,and 0.9251 and NRMSE values of 22.96,37.29,and 27.60%.The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.

关 键 词：NEURAL NETWORK ESTIMATION 

分 类 号：S636.2[农业科学—蔬菜学]