The shipment of cut flowers from Colombia and Ecuador to the United States, the biggest importer of this product in the world, has doubled in the last 20 years. One of the main constraints in cut roses production is the gray mold disease caused by the fungus Bot rytis cinerea, which can destroy the flowers, in the crop, during storage and/or shipping. Since the resistance of the fungus to conventional fungicides has been increasing, as well as the health effects in rose growers, alternative approaches for controlling the disease are needed. The effect of UV-C light on the gray mold development in cut roses was studied. Irradiation with 2,160; 1,080 and 540 J ⋅ m-2 UV-C, every 24 h for 5 days in a humid chamber, did not harm the roses. Instead, as seen by image analysis, a highly significant reduction of the area of the lesions by the disease and of the fungus germination was obtained at 1,080 J ⋅ m-2. The addition of a 4-h dark period to the irradiation did not improve the effect of UV-C on the disease. The results of this work potentiate the use of UV-C light in the agro-industry as a low-cost and non-invasive alternative method to control diseases. They also reflect the application of optical approaches as image analysis in the evaluation of important agricultural features.

The response of fiber specklegram sensors (FSSs) is given as function of variations in the intensity distribution of the modal interference pattern or speckle pattern induced by external disturbances. In the present work, the behavior of a FSS sensing scheme under thermal perturbations is studied by means of computational simulations of the speckle patterns. These simulations are generated by applying the finite element method (FEM) to the modal interference in optical fibers as a function of the thermal disturbance and the length of the sensing zone. A correlation analysis is performed on the images generated in the simulations to evaluate the dependence between the changes in the speckle pattern grains and the intensity of the applied disturbance. The numerical simulation shows how the building characteristic of the length of sensing zone, combined with image processing, can be manipulated to control the metrological performance of the sensors.

The rapid and accurate identification of pathogenic spores is essential for the early diagnosis of diseases in modern agriculture. Gray mold disease, caused by Botrytis cinerea, is a significant threat to several crops and is traditionally controlled using fungicides or, alternatively, by UV-C radiation. Classically, the determination of conidial germination percentage, a key indicator for assessing pathogen viability, has been a manual, time-consuming, and error-prone process. This study proposes an approach based on deep learning, using one-stage detectors to automate the detection and counting of germinated and non-germinated conidia in microscopy images. We trained and assessed the performance of three models under several metrics: YOLOv8, YOLOv11, and RetinaNET. The results show that these three architectures provide an efficient and accurate solution for the recognition of gray mold conidia viability. Selecting the best model, we performed the task of detecting and counting conidia for determining the germination percentage on samples treated with different UV-C radiation dosages. The results show that these deep-learning models achieved counting accuracies that closely matched those obtained with conventional manual methods, yet they delivered results far more rapidly. Because they operate continuously without fatigue or operator bias, these models begin to open possibilities, after widening field tests and datasets, for efficient and fully automated monitoring pipelines for disease management in the agro-industry.

This study introduces a Fiber Specklegram Sensor (FSS) design for temperature measurement utilizing a single-multi-single mode optical fiber configuration. FSS technology offers unique advantages for potential development into portable sensing systems. This investigation explores an FSS design compatible with miniaturization by optimizing the interrogation system based on the relationship between speckle grain size and filtering fiber area. Temperature variations are detected through fluctuations in optical power at the sensor output, offering a simple and reduced interrogation scheme. The performance of the FSS is assessed through post-calibration measurements compared to a commercial Fiber Bragg Grating (FBG) sensor, demonstrating comparable results. These findings pave the way for compact and portable fiber optic temperature sensing systems leveraging FSS advantages while maintaining compatibility with established fiber optic measurements.

Neural quantum states (NQSs) are powerful variational ansätze capable of representing highly entangled quantum many-body wavefunctions. While the average entanglement properties of ensembles of restricted Boltzmann machines are well understood, the entanglement structure of autoregressive NQSs such as recurrent neural networks and transformers remains largely unexplored. We perform large-scale simulations of ensembles of random autoregressive wavefunctions for chains of up to 256 spins and uncover signatures of transitions in their average entanglement scaling, entanglement spectra, and correlation functions. We show that the standard softmax normalization of the wavefunction suppresses entanglement and fluctuations, and introduce a square modulus normalization function that restores them. Finally, we connect the insights gained from our entanglement and activation function analysis to initialization strategies for finding the ground states of strongly correlated Hamiltonians via variational Monte Carlo.