Assessing plant population of cotton is important to make replanting decisions in low plant density areas, prone to yielding penalties. Since the measurement of plant population in the field is labor intensive and subject to error, in this study, a new approach of image-based plant counting is proposed, using unmanned aircraft systems (UAS; DJI Mavic 2 Pro, Shenzhen, China) data. The previously developed image-based techniques required a priori information of geometry or statistical characteristics of plant canopy features, while also limiting the versatility of the methods in variable field conditions. In this regard, a deep learning-based plant counting algorithm was proposed to reduce the number of input variables, and to remove requirements for acquiring geometric or statistical information. The object detection model named You Only Look Once version 3 (YOLOv3) and photogrammetry were utilized to separate, locate, and count cotton plants in the seedling stage. The proposed algorithm was tested with four different UAS datasets, containing variability in plant size, overall illumination, and background brightness. Root mean square error (RMSE) and R² values of the optimal plant count results ranged from 0.50 to 0.60 plants per linear meter of row (number of plants within 1 m distance along the planting row direction) and 0.96 to 0.97, respectively. The object detection algorithm, trained with variable plant size, ground wetness, and lighting conditions generally resulted in a lower detection error, unless an observable difference of developmental stages of cotton existed. The proposed plant counting algorithm performed well with 0–14 plants per linear meter of row, when cotton plants are generally separable in the seedling stage. This study is expected to provide an automated methodology for in situ evaluation of plant emergence using UAS data.

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

Tar spot is a foliar disease of corn characterized by fungal fruiting bodies that resemble tar spots. The disease emerged in the U.S. in 2015, and severe outbreaks in 2018 caused an economic impact on corn yields throughout the Midwest. Adequate epidemiological surveillance and disease quantification are necessary to develop immediate and long-term management strategies. This study presents a measurement framework that evaluates the disease severity of tar spot using unmanned aircraft systems (UAS)-based plant phenotyping and regression techniques. UAS-based plant phenotypic information, such as canopy cover, canopy volume, and vegetation indices, were used as explanatory variables. Visual estimations of disease severity were performed by expert plant pathologists per experiment plot basis and used as response variables. Three regression methods, namely ordinary least squares (OLS), support vector regression (SVR), and multilayer perceptron (MLP), were used to determine an optimal regression method for UAS-based tar spot measurement. The cross-validation results showed that the regression model based on MLP provides the highest accuracy of disease measurements. By training and testing the model with spatially separated datasets, the proposed regression model achieved a Lin’s concordance correlation coefficient (ρ_c) of 0.82 and a root mean square error (RMSE) of 6.42. This study demonstrated that we could use the proposed UAS-based method for the disease quantification of tar spot, which shows a gradual spectral response as the disease develops.

Planet’s SuperDove (SD) sensors offer eight bands (seven visible, one near infrared (NIR)) at 3 m spatial and near-daily temporal resolution. The yellow (610 nm) and red-edge (705 nm) bands are valuable for retrieving water quality (WQ) parameters, supporting applications such as harmful algal bloom (HAB) and post-disaster monitoring. To enable scientific use, we assess signal-to-noise ratios (SNRs), with the highest (248:1) at 443 nm and the lowest (8:1) at 865 nm, and other visible bands ranging from 26:1–98:1. We cross-calibrated SD with Sentinel-2 Multi-Spectral Imager (MSI) using near-simultaneous observations over aquatic environments by comparing top-of-atmosphere (TOA; ρt) reflectance across five shared visible bands (443, 490, 565, 665, and 705 nm), and derived calibration coefficients through linear regression. Before calibration, SD-MSI median ρt differences ranged from ∼0.7–13%, with highest differences at 705 nm. After applying the calibration, these differences reduced to −0.07% to −2.2%, including improvements at 665 nm (from ∼8% to −2.2%) and 705 nm (from ∼13% to −0.1%). Differences in atmospherically corrected remote sensing reflectance (Rrs) also decreased from 16%–95% to 8%–72% post-calibration, with 565 nm showing the lowest (∼8%) and 705 nm the highest (∼72%) residual difference. Remaining Rrs discrepancies are attributed in part to SD’s inter-sensor differences and uncertainties in atmospheric correction. We qualitatively compared chlorophyll-a (Chla) and Secchi-disk depth (Zsd) WQ products from SD and MSI, including a time-series analysis focused on the Dixie Fire and subsequent algal bloom in Lake Almanor (Sept–December 2021). The products captured expected trends, highlighting SD’s potential for WQ monitoring, while elevated uncertainties in ρt and Rrs suggest the need for improved calibration stability and atmospheric correction.