Sprouts are vehicles of foodborne diseases caused by pathogens such as Salmonella. The aim of this study was to evaluate thermal and chemical treatments applied as a hurdle approach to red... HIGHLIGHTS A hurdle approach reduces Salmonella concentration >5 log on alfalfa and broccoli seeds. Germination rates over 90% were reached on alfalfa and broccoli seeds after sequential treatments...

Insect pests remain a major threat to agricultural productivity, particularly in open-field cropping systems where conventional monitoring methods are labor-intensive and lack scalability. This study presents the design, implementation, and field evaluation of a neural network-guided smart trap specifically developed to monitor and selectively capture nocturnal insect pests under real agricultural conditions. The proposed trap integrates light and rain sensors, servo-controlled mechanical gates, and a single-layer perceptron neural network deployed on an ATmega-2560 microcontroller by Microchip Technology Inc. (Chandler, AZ, USA). The perceptron processes normalized sensor inputs to autonomously decide, in real time, whether to open or close the gate, thereby enhancing the selectivity of insect capture. The system features a removable tray containing a food-based attractant and yellow and green LEDs designed to lure target species such as moths and flies from the orders Lepidoptera and Diptera. Field trials were conducted between June and August 2023 in La Barca, Jalisco, Mexico, under diverse environmental conditions. Captured insects were analyzed and classified using the iNaturalist platform, with the successful identification of key pest species including Tetanolita floridiana, Synchlora spp., Estigmene acrea, Sphingomorpha chlorea, Gymnoscelis rufifasciata, and Musca domestica, while minimizing the capture of non-target organisms such as Carpophilus spp., Hexagenia limbata, and Chrysoperla spp. Statistical analysis using the Kruskal–Wallis test confirmed significant differences in capture rates across environmental conditions. The results highlight the potential of this low-cost device to improve pest monitoring accuracy, and lay the groundwork for the future integration of more advanced AI-based classification and species recognition systems targeting nocturnal Lepidoptera and other pest insects.

In this paper, a methodological framework employing an observer-based nonlinear controller is presented for controlling the lateral velocity of a farm tractor, as well as the yaw velocity of the agricultural implement. This approach relies on measurements obtained from sensors installed on a modern farm tractor, including lateral and longitudinal accelerations, longitudinal velocity, yaw rate, steering angle, and the differential yaw rate between the farm tractor and the implement. The nonlinear observer estimates the longitudinal and lateral velocities of the vehicle, as well as the roll dynamics of the implement, and ensures the exponential convergence of the observed variables. The control objective is formulated to ensure error feedback control, guaranteeing accurate tracking of the lateral velocity and yaw rate of the farm tractor and implement, following the reference patterns for these variables. The reference system is modeled as an “ideal” tractor operating without attachments. To evaluate the proposed controller’s performance, two test maneuvers were conducted. The first test involved the classic U-turn maneuver, commonly executed by tractors, while the second was a double-step maneuver, a standard in ground vehicle testing. Both maneuvers were simulated using MATLAB–Simulink to evaluate the controller’s effectiveness and robustness against parameter variations.

This article presents a nonlinear control strategy designed to address parametric uncertainties in an agricultural tractor system coupled to a towed implement. The controller ensures accurate tracking of lateral and yaw velocities relative to desired reference trajectories, even under the presence of parametric variations and external disturbances. The reference trajectories are derived from an “ideal” tractor model, excluding the effects of the towed implement. A High-Order Sliding Mode (HOSM) estimator is employed to provide an estimation of disturbances, which are subsequently mitigated by the controller to maintain system stability and precision. The effectiveness of the proposed control strategy is validated through Matlab-Simulink simulations, which include a double-step steer maneuver. This maneuver tests the system’s ability to handle abrupt steering changes, providing insight into the controller’s robustness and its capacity to ensure accurate trajectory tracking in demanding conditions.

Según el Consejo Mexicano de la Carne, en el 2017, la producción nacional de carne de bovino fue de 1.915 millones de toneladas. La carne molida de bovino se ha visto implicada en numerosos brotes de enfermedad desde 1982, particularmente causados por patotipos de Escherichia coli. Durante los meses de marzo a noviembre del 2014, se analizaron 100 muestras de carne de bovino molida, obtenida de carnicerías de la zona metropolitana de Guadalajara, Jalisco, con el objetivo de aislar e identificar patotípos de Escherichia coli. Para tal efecto, se empleó el método propuesto por la Food and Drug Administration. La identificación de los patotipos fue mediante la amplificación de genes que codifican para factores de virulencia, mediante PCR multiplex. Se aislaron 76 cepas sospechosas para Escherichia coli, de 16 muestras. Solo 72.36 % (55/76) de las cepas presuntivas para Escherichia coli amplificaron el gen gadA/B, lo que confirma el género y especie. El 7.27 % (4/55) mostraron el gen aggR propio de EAEC y el 41.18 % (23/55) el gen elt que codifican para la toxina termolábil de ETEC. Es evidente el riesgo potencial que implica el consumo de carne molida de establecimientos de la zona metropolitana de Guadalajara, Jalisco.