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.

This study characterized the morphological, physical and chemical attributes of sandstone-derived soils at the Cerrado of the Piauí State, Brazil, in order to identify evolutionary standards. The study was carried out with five representative soil profiles identified as P1-RY (Typical Flavic Psychotic Neosol - Aquents), P2-PA (Typical Dystrophic Yellow Argisol - Alfisol), P3-RL (Fragmentary Litholic Distrophic Neosol - Psammenit), P4-RQ (Typical Ortic Quartzenetic Neosol - Orthents) and P5-PV (Typical Dystrophic Red Argisol - Ultisol). Soil samples were submitted laboratory analysis described morphologically. In general, the soils presented high sand content, low pH, low content of exchangeable bases and low cation exchange capacity (CEC). Organic matter governed the CEC in most cases, suggesting dependence of organic matter in the supply of charges. These soils showed a low degree of weathering, but with iron of high crystallinity. Thus, the relief and the parent material are the major important soil-forming factors at the Cerrado of the Piauí State. Moreover, these soils are young, with the soils from the Piauí Formation being more evolved. However, the sandstones from the Canindé Group apparently are providing lithological secondary minerals for the soil.

Unmanned aerial vehicles (UAVs) equipped with a global real-time kinematic navigation satellite system (GNSS RTK) could be a state-of-the-art solution to measuring gaps in sugarcane fields and enable site-specific management. Recent studies recommend the use of UAVs to map these gaps. However, low-accuracy GNSS provides incomplete or inaccurate photogrammetric reconstructions, which could easily generate an error in the gap measurement and constrain the applicability of these techniques. Therefore, in this study, we evaluated the potential of UAV RTK imagery for mapping gaps in sugarcane. To compare this solution with conventional UAV approaches, the precision and accuracy of RTK and non-RTK flights were evaluated. To increase the robustness of the research, flights were performed to map gaps found naturally in the field and with plants at different stages of development. Our results showed that the lengths of gaps identified by both RTK and non-RTK UAV imagery were similar, with differences in precision and accuracy of about 1% for both systems. In contrast, RTK was much more efficient and provides stakeholders with guidelines for accurate and precise mapping gaps, allowing them to make confident decisions on site-specific management.

This study aimed to evaluate the performance of six machine-learning models in forecasting corn (Zea mays L.) grain yield before harvest using, as input, variables in the models, some of the most-used vegetation indices (VIs) and spectral bands in the literature, as well as using data at 770 and 980 sum of degree days (SDD).The field study was carried out in a commercial area in the 2017-2018 and 2018-2019 harvests. Spectral data were obtained from Sentinel-2 satellite images and were used as input variables in the proposed models: artificial neural networks (ANN), k-nearest neighbors (KNN), random forest (RF), and support vector machine (SVM). The maximum R 2 and minimum values of mean absolute error (MAE) and RMSE were 0.89, 0.33, and 0.42 t ha −1 , respectively, for the RF algorithm using all input variables. The results obtained in the present study show that it is possible to predict corn grain yield 80 d before harvest using only VIs for the crop. Testing the various combinations of spectral bands and VIs resulted in obtaining the GREEN band and the VI global environment monitoring index (GEMI) as the best predictor variables in the present study. The use of more than one SDD did not improve the performance of the models tested. The models developed using data at 980 SDD obtained the best precision and accuracy performance both in the scenario with all model input variables and with the two best predictors. The KNN algorithm obtained the best performance in the precision and accuracy metrics for most of the scenarios studied in the present work.

Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.