Search

Acquiring data across many locations and enabling fast decision-making are strengths of hyperspectral measurements. Nevertheless, a range of data processing approaches (Savisky-Golay [SG], first derivative [FD], and normalization) and modeling frameworks (partial least squares regression [PLS], weighted k-nearest neighbor [KKNN], support vector machine [SVM], and random forest [RF]) can be applied to establish the strongest link between physical observations and hyperspectral signatures. In this study, FD emerged as the top processing technique, while SVM was the leading model for estimating mean cotton (Gossypium spp., Malvaceae) height and node counts. Conversely, the integration of FD with RF achieved the highest accuracy in predicting cotton leaf area index, canopy cover, and chlorophyll levels throughout the growing period. Furthermore, outputs from models built with both SVM and RF corresponded closely to the pseudo-CHIME satellite wavebands, a process in which field-collected hyperspectral data were adjusted to the spectral intervals of an anticipated spaceborne hyperspectral sensor. The knowledge and outcomes supplied here will equip growers and other players in the cotton sector to execute swift, well-informed actions that could foster higher productivity and sustainable intensification.

The surveillance of animal behavior is a vital tool in animal production systems. This approach to monitoring behavior can serve as an indicator of animal welfare and health status, potentially translating into superior productive outcomes. The present investigation was designed to determine the optimal accelerometer placement (on the halter compared to a neck collar) and data transmission sampling periods (from 6 up to 600 seconds) for the forecasting of behavioral repertoires, which included frequencies of water and feed consumption along with additional activities, in young beef cattle bulls under feedlot conditions. To meet the study’s goals, a suite of machine learning classifiers was implemented, including random forests, support vector machines, multilayer perceptrons, and naive Bayes. Every model examined achieved high classification performance (above 0.90) regardless of placement site, except those trained with the naive Bayes classifier. Accordingly, integrating accelerometers into collars represents a more feasible solution for on-animal use, given the relative ease of application compared with halter mounting. The adoption of a larger dataset (i.e., shorter sampling windows) did not substantially improve the performance of the fitted models. Consequently, operating with datasets of reduced observational density is more beneficial, as it can reduce both the computational load and time investment required for model fitting while simultaneously extending the device’s battery longevity.