Abstract
Abstract
Background
The objective of this study is twofold. First, ascertain the important variables that predict tomato yields from plant height (PH) and vegetation index (VI) maps. The maps were derived from images taken by unmanned aerial vehicles (UAVs). Second, examine the accuracy of predictions of tomato fresh shoot masses (SM), fruit weights (FW), and the number of fruits (FN) from multiple machine learning algorithms using selected variable sets. To realize our objective, ultra-high-resolution RGB and multispectral images were collected by a UAV on ten days in 2020’s tomato growing season. From these images, 756 total variables, including first- (e.g., average, standard deviation, skewness, range, and maximum) and second-order (e.g., gray-level co-occurrence matrix features and growth rates of PH and VIs) statistics for each plant, were extracted. Several selection algorithms (i.e., Boruta, DALEX, genetic algorithm, least absolute shrinkage and selection operator, and recursive feature elimination) were used to select the variable sets useful for predicting SM, FW, and FN. Random forests, ridge regressions, and support vector machines were used to predict the yield using the top five selected variable sets.
Results
First-order statistics of PH and VIs collected during the early to mid-fruit formation periods, about one month prior to harvest, were important variables for predicting SM. Similar to the case for SM, variables collected approximately one month prior to harvest were important for predicting FW and FN. Furthermore, variables related to PH were unimportant for prediction. Compared with predictions obtained using only first-order statistics, those obtained using the second-order statistics of VIs were more accurate for FW and FN. The prediction accuracy of SM, FW, and FN by models constructed from all variables (rRMSE = 8.8–28.1%) was better than that from first-order statistics (rRMSE = 10.0–50.1%).
Conclusions
In addition to basic statistics (e.g., average and standard deviation), we derived second-order statistics of PH and VIs at the plant level using the ultra-high resolution UAV images. Our findings indicated that our variable selection method reduced the number variables needed for tomato yield prediction, improving the efficiency of phenotypic data collection and assisting with the selection of high-yield lines within breeding programs.
Funder
Japan Science and Technology Agency
Japan Society for the Promotion of Science
Publisher
Springer Science and Business Media LLC
Subject
Plant Science,Genetics,Biotechnology
Reference48 articles.
1. Li N, Wu X, Zhuang W, Xia L, Chen Y, Wu C, et al. Tomato and lycopene and multiple health outcomes: umbrella review. Food Chem. 2021;343:128396.
2. FAO. FAOSTAT. http://www.fao.org/faostat/en/#home. Accessed 18 Jan 2021.
3. Ramasamy S, Ravishankar M. Integrated pest management strategies for tomato under protected structures. In: Sustainable management of arthropod pests of tomato. Elsevier; 2018, pp. 313–322.
4. Islam J, Kabir Y. Effects and mechanisms of antioxidant-rich functional beverages on disease prevention. In: Functional and medicinal beverages. Elsevier; 2019, pp. 157–198.
5. Megan Ware RDNLD. Everything you need to know about tomatoes. https://medilinkblog.com/everything-you-need-to-know-about-tomatoes/. Accessed 27 Dec 2020.
Cited by
26 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献