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The delineation of hazelnut plantations has the potential to assist land management and utilization strategies, thereby enabling the advancement of collaborative precision agriculture infrastructures. This investigation addresses the recognition of hazelnut cultivation by leveraging both optical and radar remote sensing. A comparative appraisal of various Machine Learning methodologies is conducted. The framework put forward harnesses multi-temporal information from the Sentinel-1 and Sentinel-2 archives, acquired over several years and processed in cloud computing environments. A data collection encompassing 62,982 annotated instances is furnished, with 16,561 cases assigned to the ‘hazelnut’ category and 46,421 to the ‘other’ category, gathered from 8 disparate geographic sectors within the province of Viterbo. Two distinct comparative assessments are performed: initially, a Nested 5-Fold Cross-Validation routine is used to fit, tune, and compare multiple Machine Learning classifiers confined to a single sector. In a follow-on trial, the classifiers were fitted on one sector and then validated against the remaining seven geographic sectors. The investigation confirms that AI-driven interpretation of Sentinel-1 and Sentinel-2 imagery is a viable solution for hazelnut cartography. The outcomes indicate that Random Forest delivers the most robust generalizability, achieving peak results in the second trial, with 96% accuracy and an F1 score of 91% for the ‘hazelnut’ group.

Quantifying fruit mass and volume manually is a laborious, repetitive task that can hinder production scheduling. This investigation developed a computer vision framework to predict the volume and mass of baobab fruits from single-view photographs captured with affordable, widely accessible cameras, including smartphones. The baobab fruits were sourced from two experimental sites situated within the savanna ecological zone. Following image capture, the fruits were detected and segmented with an accuracy exceeding 97%. The segmented outputs were converted to binary format, from which two-dimensional (2D) attributes—namely the segmented area, centroid, bounding box, equivalent diameter, and major diameter—were derived. Volume estimations were generated from the 2D attributes by employing random forest, linear, polynomial, and radial support vector machine regressors. Every model achieved strong goodness-of-fit, yet the random forest regressor performed best, with an R2 of 99.8%. The linkage between mass and volume conformed to a quadratic expression (mass = 38.23 + 0.25 × volume + 4.49e−05 × volume2) and achieved an R2 of 92%. Correlations were substantiated through graphical plots and statistical evaluations, with credible intervals for the point estimates derived from the posterior distributions of their respective samples. This finding spotlights the potential to deploy artificial intelligence techniques in a less restrictive environment to support ecological studies and agricultural oversight. Commercial firms that manufacture baobab powder and seed oil ought to adopt these models to streamline production planning. Acquiring deeper insight into how climatic gradients shape the morphological properties of baobab fruits would help refine the model.

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.

A large segment of India’s rural population depends on agriculture for its livelihood, yet difficulties with manual processes and disease management persist. To address these problems, this study presents an automated framework for detecting leaf damage and identifying plant diseases using advanced deep learning methods. The introduced technique unfolds in six steps: first, YOLOv8 is harnessed to pinpoint regions of interest from drone-acquired images; second, DeepLabV3+ is applied for background stripping to aid disease categorization; third, a CNN model is deployed for reliable disease sorting, registering strong training and validation accuracy figures (96.97% and 92.89%, respectively); fourth, UNet-based semantic segmentation is utilized for meticulous pixel-scale damage identification, with an assessment accuracy of 99%; fifth, disease severity is gauged; and sixth, customized remedial measures are put forward according to the disease category and the extent of damage. Experimental trials using the Plant Village dataset confirm the technique’s ability to detect various defects across crops, including apple, tomato, and corn. This mechanized strategy carries significant potential to boost agricultural output and strengthen disease management both within India and globally.

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.