Agriculture

Crop production in the desert Southwest of the United States, as well as in other arid and semi-arid regions, requires tools that provide accurate crop evapotranspiration (ET) estimates to support efficient irrigation management. Such tools include the web-based OpenET platform, which provides real-time ET data generated from six satellite-based models, their Ensemble, and a field-based system (LI-710, LI-COR Inc., Lincoln, NE, USA). This study evaluated simulated ET (ET_SIM) of cotton (Gossypium hirsutum L.) derived from OpenET models (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop), their Ensemble approach, and LI-710. Field data were utilized to estimate cotton ET using the soil water balance (SWB) method (ET_SWB) from June to October 2025 in Gila Bend, AZ, USA. Four evaluation metrics, the normalized root-mean-squared error (NRMSE), mean bias error (MBE), simulation error (S_e), and coefficient of determination (R²), were employed to evaluate the performance of OpenET models, their Ensemble, and the LI-710 in estimating cotton ET. Statistical analysis indicated that the ALEXI/DisALEXI, geeSEBAL, and PT-JPL models substantially underestimated ET_SWB, with simulation errors ranging from −26.92% to −20.57%. The eeMETRIC, SIMS, SSEBop, and Ensemble provided acceptable ET estimates (22.57% ≤ NRMSE ≤ 29.85%, −0.36 mm. day⁻¹ ≤ MBE ≤ 0.16 mm. day⁻¹, −7.58% ≤ S_e ≤ 3.42%, 0.57 ≤ R² ≤ 0.74). Meanwhile, LI-710 simulated cotton ET acceptably with a slight tendency to overestimate daily ET by 0.21 mm. A strong positive correlation was observed between daily ET_SIM from LI-710 and ET_SWB, with S_e and NRMSE of 4.40% and 23.68%, respectively. Based on our findings, using a singular OpenET model, such as eeMETRIC, SIMS, or SSEBop, the OpenET Ensemble, and the LI-710 can offer growers and decision-makers reliable guidance for efficient irrigation management of late-planted cotton in arid and semi-arid climates.

The effects of pelleting a total mixed ration (TMR) for dairy sheep were investigated in an experiment involving 24 lactating Assaf ewes, which were assigned to two groups and fed the same TMR ad libitum, offered either in pelleted (PTMR group, n = 12) or in unpelleted form (CTMR group, n = 12). The experiment lasted 28 days, during which feed intake, eating behavior (including meal frequency and size, meal duration, eating rate, between-meal interval), and milk yield were recorded daily. Body weight (BW) was recorded on days 1 and 28 and milk samples were collected on days 1, 8, 15, 22 and 28 for milk composition analysis. Blood acid-base status was determined at the beginning and at the end of the trial. Ewes fed the CTMR diet exhibited (p < 0.05) a higher meal frequency and longer meal duration, along with a smaller meal size and slower eating rate. However, feed intake in this group was less than that in ewes fed PTMR only during the final two weeks of the experimental period. Total eating time was also longer (p < 0.001) in the CTMR group, whereas the average time between meals was shorter (p < 0.002). No differences (p > 0.05) were observed between dietary treatments in blood acid-base status, milk yield or milk composition. However, a diet x day interaction (p < 0.05) was detected for milk yield, as during the last 2 weeks of the experimental period the ewes fed the PTMR yielded more milk than those fed the CTMR. Feed conversion ratio did not differ between groups (p > 0.05), but body weight loss was greater in ewes fed the CTMR diet (−3.00 vs. −0.58 kg; p < 0.05). A trend toward improved feed efficiency was observed in the PTMR group when calculated based on milk yield corrected for that theoretically derived from the mobilization of body reserves (1.98 vs. 1.41 g DMI/kg milk; p = 0.077), with estimated contributions from body reserves of 485 g/day in the CTMR group and 70 g/day in the PTMR group. In conclusion, the use of pelleted total mixed rations in high-yielding dairy ewes enhances feed intake, feed efficiency, milk yield, and energy balance without adversely affecting milk composition or animal health in the short term.

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops.