Analysing Maize Plant Height Using Unmanned Aerial Vehicle (UAV) RGB based on Digital Surface Models (DSM)

Abstract

Maize (Zea mays L.) is one of the essential agricultural products in Papua Province of Indonesia, specifically in the three largest maize producing regions, namely Nabire Regency, Biak Numfor Regency and Merauke Regency, with the number of productions of 991 tons, 764 tons, and 751 tons respectively in 2015. Unfortunately, since 2016 the secondary data on food crops productivity, including maize, has not been provided yet in the provinces statistical report, due to manual estimation methods, i.e., visual estimation. On the other side, the number of populations in this Province has a slight increase, from 2.97 million people in 2012 to 3.38 million in 2019. Further, approximately 1.20 million people are employed in the agricultural sector. Considerable population growth will intensify the demand for food stock and other utilization of food crops in this region; hence, relevant research in food crops needs to be considered. One of the dominant factors in the yield potential of maize is plant height, since it is associated with fertilizer, seed, and soil treatment and predicts yield area. Therefore, this study aims to analyse the plant height, particularly maize plant based on a digital surface model (DSM) derived from Unmanned Aerial Vehicle (UAV) Red Green Blue (RGB) images. The crop was monitored during the second and third week of January 2022 and then, processed using pix4d Mapper software to produce the DSM, Digital Terrain Model (DTM), and orthomosaic. Then, the Geographical Information System (GIS) software, and an open-source software, namely Python were used to estimate the plant height. Next, the results were assessed statistically to examine the validation, the strong correlation coefficient of the estimation to the actual height that obtained from UAV and ground-based plant height data. The findings will help to support the prior decision support on estimation of maize production in Papua Province.

Potential of Underutilized Sago for Bioenergy Uses

“…For instance, the Indonesian Government in Presidential Decree No 5/2006 states the target of 25% share of renewable energy (RE) by 2021; unfortunately, the RE production is only about 3%. The situation explains why bioenergy development in this country is supported and encourages the potentiality of waste from raw materials for energy utilization [28]. Currently, Indonesia has been producing and marketing biodiesel from palm oil.…”

Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning

“…The initial UAV flight performed on the day of maize sowing was utilized as the reference digital terrain model (DTM). For the subsequent time points (20,29,37,44,50,61, and 71 days after sowing), image processing led to the creation of digital surface models (DSMs) [14,15,[38][39][40]. These DSMs were then input into QGIS software, where Equation ( 1) was applied to generate the DEM values corresponding to each assessment date.…”

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

“…Multiple examples of accurate plant height estimates using UAV imagery have been shown in crops such as barley (Hordeum vulgare L.) (Bendig et al, 2014;Herzig et al, 2021), cabbage (Brassica oleracea var. capitata L.) (Moeckel et al, 2018), cotton (Gossypium hirsutum L.) (Feng et al, 2019;Liu et al, 2020), eggplant (Solanum melongena L.) (Moeckel et al, 2018), faba bean (Vicia faba L.) (Ji et al, 2022), maize (Anthony et al, 2014;Geipel et al, 2014;Grenzdörffer, 2014;Su et al, 2019;Varela et al, 2017;Anderson et al, 2019;Malambo et al, 2018;Shi et al, 2016;Tirado et al, 2020;Adak, Murray, Božinović, et al, 2021;Letsoin et al, 2023), potato (Solanum tuberosum L.) (de Jesus Colwell et al, 2021;Xie et al, 2022;Njane et al, 2023), rapeseed (Brassica napus L.) (Xie et al, 2021), sorghum (Sorghum bicolor L.) (Chang et al, 2017;Shi et al, 2016;Watanabe et al, 2017;Gano et al, 2021), soybean (Glycine max L.) (Li et al, 2022), tomato (Solanum lycopersicum L.) (Moeckel et al, 2018), and wheat (Triticum aestivum L.) (Holman et al, 2016;Madec et al, 2017;Michalski et al, 2018;Volpato et al, 2021). Studies on plant height using UAVs have shown variable levels of success when compared to manual measurements due to plant structure, field layout, and improvements in best practices as more research was completed (Holman et al, 2016;Sweet et al, 2022).…”