Multispectral Analysis of Small Plots Based on Field and Remote Sensing Surveys—A Comparative Evaluation

Abstract

Remote sensing is an efficient method of monitoring experiments rapidly and by enabling the collection of significantly more detailed data, than using only field measurements, ensuring new possibilities in scientific research. A small plot field experiment was conducted in a randomized block design with winter oat (Avena sativa L.) varieties in Debrecen, Hungary in the 2020/2021 cropping year. Multiple field measurements and aerial surveys were carried out examining the response of oat on Silicon and Sulfur foliar fertilization treatments thereby monitoring their effects on the physiology, production and stress tolerance. Parallel application of in situ (elevation, soil pH, NDVI, SPAD, chlorophyll content) and aerial (NDVI, NDRE) surveys including unmanned aerial vehicles (UAVs) provided a diverse source of data for evaluation. Both the oat varieties (88.9%) and the foliar fertilization treatments (87.5%) were correctly classified and clearly separated with the discriminant analysis based on measured data. The Pearson correlation analysis showed a very strong positive connection (r = 0.895–1.00) between the NDVI values measured using a hand-held system and UAV-installed camera, except the third measurement time, where the correlation was weaker (r = 0.70). Our results indicate that field experiments can be effectively supported by UAVs.

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

“…These are the two of the most well-known and globally documented vegetation indices, with very wide application and high accuracy in monitoring the properties of vegetation in previous studies [38][39][40]. The differences of vegetation indices over the study period were evaluated using the paired t-test, calculated from all combinations of consecutive NDVI and NDRE observations.…”

Use of machine learning for monitoring the growth stages of an agricultural crop

“…17 Meanwhile, a variety of methods including articial neural network (ANN), partial least squares regression (PLSR), random forest (RF), extreme learning machine (ELM) support vector machine (SVM) and convolutional neural network (CNN) have been used to estimated crop yield and biomass. [19][20][21] For example, four machine-learning algorithms were used to build oat biomass estimation models using a variety of VI's derived from UAV-based multispectral imagery. 19 The machine learning models demonstrated provided promising results at estimating biomass as part of an oat breeding programme.…”