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The increasing menace of climate change constitutes a profound obstacle for contemporary farming, carrying grave implications for worldwide food availability. The effect of altering climatic parameters on agricultural output, particularly for essential staple commodities, fuels worry regarding prospective harvests. This investigation explores the likely repercussions of climate change on cotton cultivation by merging archival climate records, Global Climate Model (GCMs, CMIP3) forecasts, and cotton production statistics. A broad spectrum of machine learning (ML) methodologies is applied, including multiple regression, k-nearest neighbors (KNN), boosted tree techniques, and several variants of artificial neural networks (ANNs), to delve into the intricate linkage between climatic drivers and cotton productivity. The models are built and verified using climatic and crop output data obtained from three districts within Punjab, Pakistan, across the timeframe 1991 to 2020. To anticipate future production outcomes, climatic projections from General Circulation Models (GCMs) are refined via downscaling under the SRA1B, A2, and B1 emission trajectories, facilitating outlooks extending to 2050. The outcomes reveal that precipitation exerts a trivial influence on cotton output (R = 0.0002), while peak temperature (R = -0.183) stands out as the foremost climatic determinant shaping yield, trailed by lowest temperature (R = 0.248). Of the tested architectures, the generalized feedforward (GFF) network delivered the optimal results (R = 0.960, MSE = 0.110, NMSE = 0.187, MAE = 0.269), exceeding the performance of the probabilistic neural network (PNN), KNN, multilayer perceptron (MLP), and boosted trees. By comparison, linear regression (LR) and multiple regression formulations registered weaker predictive power. The trustworthiness of GFF and KNN for generating yield projections (R² = 0.892, 0.861) endorses their suitability for reliable forecasting. The study calculates a 4.5% contraction in cotton harvest by 2050, measured against the peak recorded yield for the locality, stressing the footprint of climate change on cotton farming and its latent hazard to food supply. Nonetheless, the flexible strength of the ANN (GFF) frameworks under varying climate storylines delivers auspicious means for weaving ML into climate-adaptive cultivation regimes, advancing durable agrarian safety and curbing the detrimental fallout of climate shifts on nutrition provision.

Reliable estimation of crop yield is vital for refining farming operations and guaranteeing food supply. This investigation tested the capacity of machine learning techniques to estimate tomato yield using meteorological measurements, spectral reflectance bands, and vegetation indices subjected to different nitrogen levels and bio-stimulant applications to generate variation in crop development. UAV-mounted sensors captured spectral information across seven acquisition dates spanning October 27 to December 15, 2023, matching critical developmental windows: vegetative establishment (acquisition 1), anthesis (acquisitions 2 and 3), fruit set and enlargement (acquisitions 4, 5, and 6), and initial maturation (acquisition 7). Discriminating explanatory variables were selected via the Pearson correlation metric (r > 0.65, P < 0.05), incorporating Near Infrared (NIR), Red Edge, and Red wavebands, together with vegetation indices comprising NDVI, GNDVI, NDRE, and SAVI. Remotely sensed canopy signatures gathered throughout the fruit set and enlargement window (acquisitions 5 and 6) displayed the tightest association with harvested yield (r = 0.66–0.74), reinforcing the necessity of spectral intelligence from the middle to latter parts of the cycle. When comparing predictive algorithms, both linear regression (LR) and XGBoost yielded the strongest predictive skill, producing root mean squared error (RMSE) figures of 16.13 kg and 16.15 kg, respectively, matched with R² values reaching 0.63. Support vector machine (SVM) and decision tree (DT) implementations also delivered competent outcomes, achieving RMSE scores of 17.15 kg and 17.18 kg, respectively. By comparison, the deep learning architecture lagged notably (RMSE = 23.49 kg, R² = 0.23), plausibly attributable to data scarcity. This research spotlights the prognostic capability inherent in spectral wavebands and underscores the centrality of growth-stage-aligned spectral acquisition for yield forecasting.

Precise categorization of avocado ripeness plays a vital role in strengthening post-harvest operations and curbing waste throughout agricultural supply networks. The present study centers on constructing a resilient ensemble classification framework employing spectral readings from 120 kilograms of Buccaneer avocados procured from the Royal Project in Chiang Mai, Thailand. The specimens were assessed via near-infrared (NIR) spectroscopy at 18 wavelengths. Five distinct machine learning models—Random Forest, Decision Tree, XGBoost, Gradient Boosting, and Gaussian Mixture Model—were separately trained and subsequently fused into a unified ensemble. Four distinct algorithms were deployed to fine-tune the weighting of each model: Bayesian optimisation, differential evolution, particle swarm optimisation, and grid search. Evaluation metrics covering accuracy, precision, recall, F1-score, confusion matrices, and ROC curves were applied. Grid Search yielded the strongest classification outcomes, securing an accuracy of 82.5% alongside an F1-score of 85.3%, emphasizing the value of weight-refined ensemble learning relative to standalone classifiers. This investigation delivers a scalable and transparent pipeline for non-destructive ripeness identification. The outcomes, notwithstanding constraints related to overfitting and dependency on spectral data integrity, pave the way for eventual real-time field deployment in agriculture.

The quantification of actual evapotranspiration (ETₐ) is fundamental to sound water resource governance, yet current estimation methods pose significant constraints. Established methodologies, among them eddy covariance and energy balance computations reliant on remote sensing, are frequently beset by prohibitive costs, patchy spatial and temporal coverage, and deteriorating forecast fidelity, particularly for older empirical constructs. Although machine learning has emerged as a viable solution, it is not without drawbacks, especially a systematic bias toward low ETₐ predictions during high-temperature episodes. We trace this deficiency to inadequate model training on the scarce but critically significant extreme ETₐ values, paired with meteorological datasets that are limited rather than massive. Within this paper, we illustrate how few-shot, meta-learning frameworks (MAML), purpose-built to deliver superior generalization when data are constrained, can eclipse elementary machine learning formulations in the upscaling of ETₐ originating from two major in-situ observation networks, namely Ameriflux and Euroflux. Working with a sparse set of remotely sensed land surface variables sourced from the METRIC-EEFlux system alongside a narrow suite of climatic drivers, we establish that the selected models yield measurable benefits when assessed under the utility-based regression philosophy, guiding meaningful real-world decisions. Our preliminary findings show that EEflux ETₐ strays markedly from in-situ tower readings from the Ameriflux and EEflux installations (R² = 39%). By comparison, MAML attains the strongest alignment with ETₐ, outperforming both elementary machine learning architectures and EEFlux (R² = 71% on the full test corpus, R² = 0.88 under the Csa climate class, R² = 0.79 under the Cfa climate class, and R² = 0.78 for the CSH vegetation category), and steadily improves without succumbing to overfitting even when trained on a comparatively modest data pool. Its elevated F2 score (96%) indicates that MAML maintains high precision and recall for atypical instances, a feature of considerable consequence for irrigation scheduling. As a noteworthy result in its own right, this investigation corroborates that even a sparse set of remotely sensed EEflux outputs contributes materially to understanding ground-referenced ETₐ and can thus serve a valuable role in environments where the procurement of abundant, high-quality data is limited.

The advancement of motion-sensor technology for observing cattle behavior has enabled farmers to assess their animals’ welfare more efficiently. Whereas the bulk of existing research focuses on single-dimensional outputs and separate behavioural classifications, superior predictive precision can be achieved by integrating intricate movements and enhancing sensor algorithms that detect multidimensional activity, that is, more than one movement executed concurrently. This article presents a machine-learning methodology for analyzing overlapping yet independent motions. The system’s output comprises automatically identified complex behaviour sequences that may be used to gauge animal welfare, forecast calving, or detect early disease indicators. The investigation pairs automated motion-sensing equipment (namely, a halter and a pedometer) developed for ruminants and sold under the name RumiWatch with video-based surveillance. It places it upon a Charolais fattening bull. Fourteen distinct categories of complex motion were catalogued: defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. Owing to the deployment of several parallel binary classifiers, the platform proved capable of identifying concurrent behavioural patterns with high fidelity. Two machine learning methods, Support Vector Classification (SVC) and Random Forest, were applied to distinguish various generalised and non-generalised motion types. Outputs produced by these two supervised learning frameworks were placed side by side for comparison. An uninterrupted forty-eight-hour video stream was labelled to serve as training material for the systems and to corroborate their predictive outputs. The performance levels of both classifiers in pinpointing specific motions, whether using both sensors or each sensor individually, and under varying parameter configurations (i.e., window size and padding), were scrutinised. Although the two classifiers yielded divergent results, the optimised parameter configurations indicated that each motion category exhibited by the subject animal was successfully identified with substantial accuracy. Further research encompassing a greater number of individual subjects and different ruminant species would broaden the knowledge base regarding improvements to the system’s efficacy and precision.

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.