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Reliable, hands-off measurement of bovine body dimensions and weight is vital to increasing output and accelerating genetic gains in tropical beef enterprises. Traditional techniques are time-consuming, demand heavy machinery, require handling livestock, induce animal stress, and are frequently unworkable for resource-constrained smallholders lacking adequate infrastructure. This paper introduces an original, smartphone-operated platform for instantaneous body dimension capture and weight forecasting in Brahman cattle, driven by a cloud-based artificial intelligence (AI) engine. A corpus of 12,660 lateral photographs was assembled and tagged for hip depth (HD) and body length (BL), with YOLOv11 convolutional neural network variants undergoing training and evaluation. The YOLOv11m configuration achieved the highest metrics (precision: 99.85%, recall: 100%, F1-score: 99.92%, IoU: 97.68% ± 1.31%), and the automatic extractions displayed tight concordance with manual ImageJ-derived figures (MAPE < 0.4%). HD and BL were strongly intercorrelated in both sexes (r = 0.98–0.99) and moderately indicative of body weight (r = 0.57–0.59). A multiple regression formula incorporating HD and BL delivered the most precise body weight predictions (MAE = 43.44 kg; MAPE = 8.91%). The system was fielded via a LINE messaging chatbot interface, allowing users to upload cattle photos and receive immediate dimensional outputs and weight approximations straight to their smartphone—doing away with any need for bespoke hardware. This inexpensive, easy-to-operate AI instrument supplies a scalable avenue for digital phenotyping, herd oversight, and evidence-based selection in smallholder environments. The concept exhibits substantial capacity to enable data-informed management and enduring productivity improvements in tropical beef networks.

Crop production around the globe is increasingly at risk from invasive organisms, particularly the Fall Armyworm (FAW, Spodoptera frugiperda), which has caused severe deterioration of maize harvests across both Africa and Asia starting in 2016. To enable targeted pest control, an adaptive Echo State Network (ESN) was constructed that projects yearly maize output while factoring in FAW burden. A 15-year (2010–2024) monthly repository was built, integrating vegetation indicators from satellites, field-level meteorological measurements, soil nutrient readings, and FAW observation tallies. The intensity of FAW infestation is represented on a continuum from 0 to 100, derived by fusing monthly trap catch figures (40%) with larval concentration (60%). The ESN is initially calibrated on the entire body of environmental features to estimate crop performance. Next, isotonic regression is deployed to link infestation intensity with the ESN's positive residual errors, yielding a monotonic penalty mapping. This mapping captures the magnitude of output depression at varying infestation levels. When generating estimates, the inferred penalty is applied to the unadjusted ESN projection, modifying harvest forecasts to incorporate pest-driven losses without changing the foundational ESN configuration. In testing across held-out folds, the FAW-sensitive ESN yields an R² near 0.55 and cuts forecast discrepancies by as much as 67% compared to unadjusted reference models, faithfully reproducing documented harvest declines beyond 20% during major infestation episodes. The approach beats standard regression and deep neural architectures by comparable margins. It supports agriculturalists in directing countermeasures toward high-threat areas, lowering both chemical input volumes and operational expenditures. These outcomes emphasize its promise as a forward-looking alert mechanism for pinpointed actions that curtail agrochemical usage and improve input distribution. Ongoing field assessments will gauge its capacity to scale and its tangible efficacy within maize-growing zones affected by FAW.

Agricultural output and the assurance of food supplies depend fundamentally on plant breeding. A new chapter in this field has been opened by Genomic Selection, a technology distinguished by its power to exploit variation across the entire genome for predictive purposes. This technique makes it unnecessary to have pre-existing knowledge of the genes controlling particular characteristics. Yet, the enormous scale of genomic information, contrasted with the usually scarce set of phenotypic observations, frequently leads to the “curse of dimensionality,” in which classic statistical approaches, machine learning, and deep learning architectures tend to memorize noise rather than learn patterns, yielding poor predictive performance. To address this barrier, we propose a consolidated Variational auto-encoder-based Multi-task Genomic Prediction model (VMGP) that fuses a self-supervised process of shrinking and rebuilding genomic data with several simultaneous forecasting objectives. This method provides a durable solution, offering a powerful inferential engine that has undergone rigorous verification across publicly available datasets for wheat, rice, and maize. The model we have built displays extraordinary proficiency in forecasting across multiple phenotypes and varying environments, effectively mastering the challenges of genomic selection spanning different populations and highlighting its distinct capabilities and practical importance. In addition, by fusing VMGP with tools for model transparency, we can efficiently sift through and prioritize influential single-nucleotide polymorphisms, thereby boosting prediction accuracy and putting forward avenues for potentially more economical genotyping workflows. The VMGP framework, characterized by its clean design, strong forecasting capabilities, and publicly accessible source code, is well-suited for widespread rollout across plant breeding operations. It proves especially valuable for practitioners whose main focus is phenotypic projection, yet who might not be deeply versed in deep learning methodologies or the intricacies of hyperparameter optimization.

Over the past few years, intense genetic pressure for rapid weight gain and elevated breast meat output has left the international poultry sector wrestling with the difficult condition known as woody breast (WB). This myopathy has inflicted massive economic damage, estimated at up to $200 million each year, and its fundamental cause has still not been pinpointed. Manual tactile assessment remains the standard approach for distinguishing WB fillets from regular ones, but this technique is labour-intensive and prone to human inconsistency. Hyperspectral imaging (HSI) combined with machine learning algorithms can assess fillet WB status in a non-invasive, impartial, and high-throughput manner. For the present work, a total of 250 uncooked chicken breast fillet specimens—spanning normal, mild, and severe categories—were collected, and, for the first time, the spatially unequal distribution of hardness was taken into account in the design of HSI-based processing models. This investigation not only categorized WB grades from HSI data but also formulated a regression framework linking the spectral inputs to measured sample firmness values. To build an effective classification and regression pipeline, a neural architecture search (NAS) strategy was used to construct a wide-deep neural network, christened NAS-WD. In this design, NAS was initially deployed to tune both the network layout and its hyperparameters autonomously. Classification outcomes indicated that NAS-WD achieved 95% total accuracy in classifying the three WB grades, exceeding the performance of classic machine learning methods. At the same time, the regression model linking spectral signatures to hardness achieved an R² of 0.75. This result substantially surpasses that of conventional regression techniques.

Obtaining body mass in large-scale rearing and production environments is difficult, making non-contact assessment methods indispensable. This paper introduces an original pipeline for predicting pig body mass based on an adapted mask region convolutional neural network (mask R-CNN). The adaptation deploys ResNeSt as the backbone feature-extraction architecture, aiming to bolster the capacity to derive features from imagery. A feature pyramid network (FPN) was appended to this backbone to enable feature merging across multiple scales. Within the region proposal network (RPN), a channel attention mechanism (CAM) was installed alongside a spatial attention mechanism (SAM), enabling the dynamic combination of localized characteristics with their long-range interdependencies. This configuration captures holistic contextual knowledge, ultimately improving segmentation fidelity. The adapted architecture achieved precision (P) of 90.33%, recall (R) of 89.85%, and mean average precision (MAP) of 95.21%, thereby isolating pig areas in images with notable effectiveness. Five descriptive features drawn from the imagery were examined: back area, body length, body width, average depth, and eccentricity. Leveraging the pig depth images, five regression learning algorithms—ordinary least squares (OLS), AdaBoost, CatBoost, XGBoost, and random forest (RF)—were trained to forecast mass values. AdaBoost delivered the strongest predictive performance, evidenced by a coefficient of determination (R2) of 0.987, a mean absolute error (MAE) of 2.96 kg, a mean square error (MSE) of 12.87 kg², and a mean absolute percentage error (MAPE) of 8.45%. The experimental results confirm that machine learning models can reliably predict pig mass, providing a technical foundation for the informed management of pig production facilities.

Achieving food availability, advancing the long-term viability of farming, and propelling economic progress. However, current forecasting frameworks often overlook the specific characteristics of rice yield distributions, which vary across regions, exhibit skewness, and are constrained by fixed lower and upper thresholds, conforming to a four-parameter beta distribution. The consequence is outputs that are less reliable and can potentially misguide decision-making. Furthermore, a majority of techniques fall short in modeling the intricate variable interactions that arise when satellite observations are fused with field survey measurements, an integration widely viewed as essential for boosting forecast fidelity and streamlining cultivation methods. To remedy these deficiencies, the present research proposes a four-parameter beta Generalized Linear Mixed Model (GLMM) and advances it into a four-parameter beta Generalized Mixed Effect Tree (GMET). This four-parameter beta GMET extends the four-parameter beta GLMM architecture by embedding a regression tree mechanism, thereby providing superior flexibility for representing non-linear and interaction-laden dependencies. Deploying this analytical framework on empirical data from Central Kalimantan and Karawang yields significant gains over earlier approaches, as evidenced by markedly lower AIC and RRMSE metrics. Significantly, the investigation finds that time-lagged reflectance values from bands 4 and 8 and the NDVI index captured by Sentinel-2A satellites are the dominant determinants of rice productivity, overshadowing the explanatory contribution of farmer questionnaire responses. This finding underscores the promise of remotely sensed data as a cornerstone for rice yield estimation, offering a more streamlined, less resource-intensive alternative to reliance on farmer-reported data. By further refining satellite-based technologies, upcoming initiatives in yield forecasting can realize superior efficiency and precision, thereby underpinning evidence-based choices in agricultural governance.