Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles

2022 ◽  
Vol 193 ◽  
pp. 106646
Amiratul Diyana Amirruddin ◽  
Farrah Melissa Muharam ◽  
Mohd Hasmadi Ismail ◽  
Ngai Paing Tan ◽  
Mohd Firdaus Ismail
Redmond Ramin Shamshiri ◽  
Ibrahim A. Hameed ◽  
Siva K. Balasundram ◽  
Desa Ahmad ◽  
Cornelia Weltzien ◽  

2021 ◽  
Vol 12 (1) ◽  
pp. 17
Endah Dwi Susanti ◽  
Novita Hera ◽  
Syukria Ikhsan Zam

Weed vegetation analysis is important to do in order to know the composition and structure of the vegetation to determine the appropriate weed control measures. This study aims to compare the composition and structure of weeds on peatland immature oil palm plantations and mature palm. This research was conducted from October to December 2020. Vegetation analysis was carried out using a survei method with purposive sampling technique. The parameters observed were density, frequency, dominance, important value index (INP), summed dominance ratio (SDR) and species diversity index (H'). The results showed that the composition of weeds on immature oil palm plantations was found 12 species with number of individual 847, while on mature palm found 9 species with number of individuals 980. The dominant weed structure on immature oil palm plantations is Lempuyangan with SDR values 29,9 % and mature palm is Bandotan with SDR value 23,4%. Weed diversity index on immature oil palm plantations and mature palm is categorized as high with a value of 1.50 for immature oil palm plantations and 1.65 for mature palm.

2020 ◽  
Vol 12 (15) ◽  
pp. 2431
Alexandria M. DiMaggio ◽  
Humberto L. Perotto-Baldivieso ◽  
J. Alfonso Ortega-S. ◽  
Chase Walther ◽  
Karelys N. Labrador-Rodriguez ◽  

The application of unmanned aerial vehicles (UAVs) in the monitoring and management of rangelands has exponentially increased in recent years due to the miniaturization of sensors, ability to capture imagery with high spatial resolution, lower altitude platforms, and the ease of flying UAVs in remote environments. The aim of this research was to develop a method to estimate forage mass in rangelands using high-resolution imagery derived from the UAV using a South Texas pasture as a pilot site. The specific objectives of this research were to (1) evaluate the feasibility of quantifying forage mass in semi-arid rangelands using a double sampling technique with high-resolution imagery and (2) to compare the effect of altitude on forage mass estimation. Orthoimagery and digital surface models (DSM) with a resolution <1.5 cm were acquired with an UAV at altitudes of 30, 40, and 50 m above ground level (AGL) in Duval County, Texas. Field forage mass data were regressed on volumes obtained from a DSM. Our results show that volumes estimated with UAV data and forage mass as measured in the field have a significant relationship at all flight altitudes with best results at 30-m AGL (r2 = 0.65) and 50-m AGL (r2 = 0.63). Furthermore, the use of UAVs would allow one to collect a large number of samples using a non-destructive method to estimate available forage for grazing animals.

A.A. Moykin ◽  
A.S. Medzhibovsky ◽  
S.A. Kriushin ◽  
M.V. Seleznev ◽  

Nowadays, the creation of remotely-piloted aerial vehicles for various purposes is regarded as one of the most relevant and promising trends of aircraft development. FAU "25 State Research Institute of Chemmotology of the Ministry of Defense of the Russian Federation" have studied the operation features of aircraft piston engines and developed technical requirements for motor oil for piston four-stroke UAV engines, as well as a new engine oil M-5z/20 AERO in cooperation with NPP KVALITET, LLC. Based on the complex of qualification tests, the stated operational properties of the experimental-industrial batch of M-5z/20 AERO oil are generally confirmed.

2016 ◽  
Vol 44 (3) ◽  
pp. 475-485
G. Ravichandran ◽  
P. Murugesan ◽  
P. Naveen Kumar ◽  
R.K. Mathur ◽  
D. Ramajayam

2020 ◽  
Vol 79 (11) ◽  
pp. 985-995
Valerii V. Semenets ◽  
V. M. Kartashov ◽  
V. I. Leonidov

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