Pilotless aircraft systems will reshape our critical thinking about agriculture. Furthermore, because they can drive a transformative precision and digital farming, we authoritatively review the contemporary academic literature on UAVs from every angle imaginable for remote sensing and on-field management, particularly for sugarcane. We focus our search on the period of 2016–2021 to refer to the broadest bibliometric collection, from the emergence of the term “UAV” in the typical literature on sugarcane to the latest year of complete publication. UAVs are capable of navigating throughout the field both autonomously and semi-autonomously at the control of an assistant operator. They prove useful to remotely capture the spatial-temporal variability with pinpoint accuracy. Thereby, they can enable the stakeholder to make early-stage decisions at the right time and place, whether for mapping, re-planting, or fertilizing areas producing feedstock for food and bioenergy. Most excitingly, they are flexible. Hence, we can strategically explore them to spray active ingredients and spread entomopathogenic bioagents (e.g., Cotesia flavipes and Thricrogramma spp.) onto the field wherever they need to be in order to suppress economically relevant pests (e.g., Diatraea saccharalis, Mahanarva fimbriolata, sugarcane mosaic virus, and weeds) more precisely and environmentally responsibly than what is possible with traditional approaches (without the need to heavily traffic and touch the object). Plainly, this means that insights into ramifications of our integrative review are timely. They will provide knowledge to progress the field’s prominence in operating flying machines to level up the cost-effectiveness of producing sugarcane towards solving the sector’s greatest challenges ahead, such as achieving food and energy security in order to thrive in an ever-challenging world.

The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R² = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.

The mechanized harvesting of peanut crops results in both visible and invisible losses. Therefore, monitoring and accurately determining pod maturation are essential to minimizing such losses. The objectives of this study were to (i) identify the most relevant variables for estimating peanut pod maturation and (ii) estimate two maturation indices (brown and black classes; orange, brown, and black classes) using Remote Sensing (RS) and Artificial Neural Networks (ANN), while assessing the generalization potential of the models across different areas. The experiment was carried out in two commercial peanut fields in the state of São Paulo, Brazil, during the 2021/2022 and 2022/2023 growing seasons, using the IAC 503 cultivar. Data collection began one month before the expected harvest date, with weekly intervals. Spectral variables and vegetation indices were obtained from orbital remote sensing (PlanetScope), while climatic data were retrieved from NASA POWER. For analysis, two ANN architectures were employed: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The dataset from the Cândido Rodrigues site was split into 80% for training and 20% for testing. The model was then evaluated and generalized using data from the Guariba site. Variable selection involved filtering via Principal Component Analysis (PCA) followed by the Stepwise method. Both models demonstrated high accuracy (R² ≥ 0.90; MAE between 0.06 and 0.07). Generalization tests yielded promising results (R² between 0.59 and 0.64; MAE between 0.13 and 0.17), confirming the robustness of the approach under different conditions.

Machine harvesting is an essential step of crop production, considering a dynamic operation, and is subject to losses due to several factors that affect its quality. The objective of this study was to evaluate the quality of mechanized peanut pickers in the three soil tillage operations using Statistical Quality Control (SQC) tools. We conducted the experiments in a peanut field located at 21°20′23″ S and 47°54′06″ W of Brazilian peanut farmers. We used Statistic Control Quality (SQC) experimental design to monitor peanut losses during machine harvesting. The treatments evaluated were three soil tillage operations: conventional (CT), rotary tillers (RT), and hoe (RH). The quality indicators were collected inside the picker’s bulk tank. Statistical analyses used were descriptive statistics and SQC tools (run charts, control charts, and the Ishikawa diagram). The process was considered stable for indicators: whole pods (CT, RT, and RH), broken pods (CT, RT, and RH), and hatched pods (CT, RT, and RH), while the other indicators showed points that were out of control. With the application of SQC tools, it was possible to identify the factors that caused the increase of variability in peanut harvesting, listing the points to be improved to support decision-making, always aiming to increase this operation’s quality.