Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and technological development into aerial remote sensing for distinguishing cultivars is still at an early stage of development, driving the need for further in-depth investigation. The primary objective of this study was therefore to analyze whether it could be possible to discriminate market-grade cultivars of sugarcane upon imagery data from an unmanned aerial vehicle (UAV). A secondary objective was to analyze whether the time of day could impact the expressiveness of spectral bands and vegetation indices (VIs) in the biophysical modeling. The remote sensing platform acquired high-resolution imagery data, making it possible for discriminating cultivars upon spectral bands and VIs without computational unfeasibility. 12:00 PM especially proved to be the most reliable time of day to perform the flight on the field and model the cultivars upon spectral bands. In contrast, the discrimination upon VIs was not specific to the time of flight. Therefore, this study can provide further information about the division of cultivars of sugarcane merely as a result of processing UAV imagery data. Insights will drive the knowledge necessary to effectively advance the field’s prominence in developing low-altitude, remotely sensing sugarcane.

Grain losses pose a threat to agricultural sustainability, particularly in developing countries. Mechanized harvesting is a key process in which losses occur; thus, its investigation is essential. Therefore, a systematic and meta-analytical review was conducted to encompass studies on quantitative soybean losses during harvesting in South America from the last two decades. The initial search yielded 1,094 scientific articles; however, only 25 studies met the inclusion criteria. This study provides an overview of technical aspects monitored on farms, guidelines to ensure efficient harvesting and literature gaps for further innovations. Because studies on soybean losses were predominantly based on continuous observational data and lacked methodological quality according to a quality scoring, only four studies were included in the meta-analysis. The meta-analysis results showed a significant relation between losses and the combine age, indicating that combines with over 10 years of use result in higher losses than newer ones (p ≤ 0.05). Conversely, operating at speeds exceeding 5 km h^-1 did not lead to significant increases in soybean losses (p > 0.05). Many decisions can be taken at the farm-level to reduce losses, such as proper training of workers and adequation of combine harvesters according to each crop condition. The insights described here are timely for paving the way towards innovation in harvesting systems and minimizing grain losses by understanding the critical points within the context improving yields during soybean harvesting.

Assessing planting to ensure well-distributed plants is important to achieve high yields. Digital farming has been helpful in these field assessments. However, these techniques are at most times not available for smallholder farmers or low-income regions. Thus, to contribute such producers, we developed two methods to assess intra-row spacing in commercial fields using mobile photos and simple image processing. We assessed a maize field after mechanized planting in 7 and 12 days after planting (DAP) and in two farming systems (conventional and no-till) to acquire images at height of one meter and perpendicular to the ground. In the first method, we used morphological operations based on the HSV scale and the center of mass to extract the region of interest (ROI) corresponding to the maize plant. In the second method, we used local maxima equations (Peaks) to find prominence values corresponding to the maize plant and extract their coordinates. No-till images were deleted due to excessive weeds. Thus, before acquiring the images, it is necessary to remove these elements (e.g., no-till adapted). The methods achieved an overall RMSE of 3.48 cm (0.71) between the actual and estimated spacing. Precision and recall were higher than 0.88. There was no difference between actual and estimated CV values, except in conventional tillage in 7 DAP using ROI due to leaves overlapping. The method Peaks was more accurate to detect multiple spacing but miss spacing was correctly detected in both methods. However, the larger the plant leaves, the worse the detection. Thus, our proposed methods were satisfactory and are promising for assessing planting in a remote and accessible way.