Search

This investigation puts forward a novel intelligent farming platform that merges Internet of Things (IoT) capabilities with predictive modeling and self-regulating command systems to streamline watering processes and amplify resource productivity. The suggested design employs soil wetness, thermal, and atmospheric moisture probes interfaced with an Arduino-centered microcontroller unit, allowing irrigation to be triggered autonomously according to live sensor readings. Agricultural operators can remotely oversee and adjust watering cycles through portable devices, delivering both exactness and operational simplicity. The predictive model fuses historical data with current measurements to anticipate future irrigation demands, guaranteeing moisture delivery exclusively when truly required. Empirical on-farm assessments indicated a 30 % decline in water expenditure versus standard irrigation techniques, whilst upholding ideal earth moisture parameters. In addition, the framework markedly diminished workforce and power expenses, bolstering overall functional capability. By confronting the issue of hydrological shortage and advancing conscientious resource stewardship, this intelligent platform possesses the capability to enhance harvest volumes, curtail greenhouse gas discharges, and further eco-conscious cultivation methodologies. Within water-limited geographies, deploying this technological solution constitutes a pivotal stride towards enduring agricultural viability, indicating considerable scope for extensive implementation.

The present investigation tackles several pressing difficulties in modern poultry operations, namely pathogen transmission, workforce deficits, inadequate surveillance capacity, and intensifying requirements for superior animal welfare standards. To meet these demands, an elaborate machine vision framework based on deep learning is put forward, wherein the YOLOv11 algorithm is deployed to autonomously observe and administer poultry. A collection of 330 chicken photographs was gathered from Thai agricultural facilities under a range of environmental circumstances and subsequently increased to 1,716 images by means of rotation, along with alterations to brightness and saturation parameters. Training of the YOLOv11 architecture was carried out on the NVIDIA Jetson Orin Nano Developer Kit, with performance gauged through precision, recall, and mean average precision (mAP) metrics. The architecture attained remarkably high fidelity, registering precision of 0.964, recall of 0.938, and mAP of 0.963, correspondingly. The fusion of optical with thermal imaging permitted the apparatus to circumvent limitations imposed by variable illumination and ambient settings. Outcomes from practical deployment evidenced real-time observation and tracing of numerous birds, thereby supporting disease mitigation, feed optimization, and holistic farm governance. This inquiry advances the domain of intelligent agriculture by furnishing a wholly automated, expandable, and exceedingly accurate poultry oversight infrastructure designed to foster ecological responsibility, operational productivity, and elevated animal welfare within contemporary poultry cultivation.

Interest in sustainable farming approaches has surged, especially within hyper-arid environments where acute water shortages make food security a pressing priority. This increased focus arises from the goal of agricultural independence in light of geopolitical volatility, rising population numbers, climate disruption, and the drive to decrease reliance on imported food. The present work investigates novel, data-centric methods specifically engineered for agriculture under such demanding circumstances, concentrating on the exploitation of CO₂ enrichment to amplify resource efficiency by accelerating plant development and fine-tuning input application within closed greenhouse structures. Within this study, a data-driven model predictive control (MPC) system is deployed in a greenhouse context to refine irrigation timing. The primary emphasis lies in harnessing the capability of an extreme gradient boosting (XGBoost) model to project real-time transpiration fluxes, accounting for the complex interdependencies between microclimatic variables and physiological shifts that influence transpiration. The XGBoost framework is designed to ingest microclimate observations—namely solar radiation, internal air temperature, internal relative humidity, and internal CO₂ molar fraction—as well as vegetation indices extracted from hyperspectral reflectance measurements, including NDVI, WBI, and PRI, all serving as forecasting inputs. The predictive model achieved strong fidelity, registering an R² of 97.1% alongside an RMSE of 0.417 mmol/m²/s for transpiration rate estimation. The trained XGBoost is subsequently embedded into the MPC scheme to steer irrigation delivery while upholding target soil water status. This integrated controller is then applied to govern watering tactics across two differing CO₂ supplementation settings: 400 ppm and 1000 ppm. Outcomes from this work reveal that the MPC-driven irrigation regime yields a 42.2% reduction in water use across a one-week forecast horizon when contrasted with the baseline irrigation protocol, irrespective of CO₂ level. Moreover, operating the MPC controller under the distinct CO₂ regimes shows a 34% decrease in water consumption at the 1000 ppm enrichment level relative to the 400 ppm condition. These findings highlight the value of MPC within enclosed greenhouse facilities, stressing the gains offered by advanced predictive analytics, multi-source data fusion, and continuous receding-horizon optimization for realizing superior irrigation regulation. The work equally accentuates the role of CO₂ fertilization in sealed agricultural greenhouses—particularly in zones subject to intense solar irradiance—as a potent measure for curbing water expenditure.

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.