Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning

Abstract

Multispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors. The research was conducted on five varieties of sugarcane, as a way to make a robust approach. Multispectral images were collected every 40 days and the evaluated biometric parameters were: number of tillers (NT), plant height (PH), and stalk diameter (SD). Two ML models were used: multiple linear regression (MLR) and random forest (RF). The results showed that models for predicting sugarcane NT, PH, and SD using time series and ML algorithms had accurate and precise predictions. Blue, Green, and NIR spectral bands provided the best performance in predicting sugarcane biometric attributes. These findings expand the possibilities for using multispectral UAV imagery in predicting sugarcane yield, particularly by including biophysical parameters.

UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane

“…Single bands and their mathematical combinations into VIs allow for collecting significant imagery data on agroecosystems, whether to make decisions on operations from implementation (e.g., seeding and planting) to harvesting. Researchers often exploit them in remotely assessing the agronomic performance of sugarcane for biomass ( Wang et al., 2022 ), quantitative yield ( Sumesh et al., 2021 ), and standard biometric variables, such as leaf area and height of an individual ( Oliveira et al., 2022 ). However, they still have not emphasized applying ML to UAV imagery data to predict °Brix and Purity as we focus on.…”

“…We can train an ML algorithm on a heterogenous and “messy” dataset to learn meaningful and non-duplicative patterns to solve a task automatically, accurately, and unbiasedly. Some applications of ML for sugarcane research and development available from earlier independent studies include predicting or forecasting chlorophyll content ( Narmilan et al., 2022 ), standard morphophysiological variables ( Oliveira et al., 2022 ), production of biomass ( Wang et al., 2022 ), and classify cultivation ( Nihar et al., 2022 ). We developed a new pathway by mapping spectral features to °Brix and Purity; hence we can fulfill a gap in analyzing qualitative yield while improving the addressability of a UAV for scalable aerial remote sensing.…”

Climate Change Analysis Based on Satellite Multispectral Image Processing in Feature Selection Using Reinforcement Learning

“…This training likewise quantitatively assesses image fusion quality after spectral fidelity, three-dimensional and detail travels the properties of dissimilar image feature groupings and techniques on mapping vegetation societies by dimensionality reduction and flexible selection. Oliveira et al [14] devise to forecast sugarcane biometric variables through ML processes and multi-temporal information over the examination of multi-spectral images after drone onboard devices. The study has shown 5 diversities of sugar cane, since way to brand a healthy method.…”

Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images

“…Unmanned aerial vehicles (UAV) have developed very rapidly in recent years due to their conveniences and the low cost for many applications [8,9]. It had been commonly understood that UAV remote sensing had been steadily rising into a new area of remote sensing studies in recent years.…”