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The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables. [ABSTRACT FROM AUTHOR]

This study explores the acoustic properties of composite biomaterials using a polylactic acid (PLA) matrix reinforced by plant fibers for sound insulation applications. Acoustic tests evaluated the absorption coefficient, reflection factor, and characteristic impedance, examining various configurations with different thicknesses of the composite biomaterial. The combinations of PLA/grape stem and PLA/wood straw were analyzed for their acoustic behaviors. Grape stems and wood straw were chosen because they are abundant, undervalued waste materials, especially in Italian regions like Tuscany. Therefore, using these materials in composite biomaterials could offer opportunities for valorization. The findings highlight the impact of plant fiber characteristics on acoustic properties, emphasizing the need to optimize these factors for desired acoustic outcomes. The results suggest implications for developing eco-friendly construction materials that balance environmental sustainability with performance requirements. This investigation contributes to the ongoing discourse on sustainable material utilization for acoustic purposes, reinforcing the potential for innovative and environmentally conscious building solutions. [ABSTRACT FROM AUTHOR]

The coffee leaf miner (Leucoptera coffeella) is a key pest in coffee-producing regions in Brazil. The objective of this work was to evaluate the potential of machine learning algorithms to identify coffee leaf miner infestation by considering the assessment period and Sentinel-2 satellite images generated on the Google Earth Engine platform. Coffee leaf miner infestation in the field was measured monthly from 2019 to 2023. Images were selected from the Sentinel-2 satellite to determine 13 vegetative indices. The selection of images and calculations of the vegetation indices were carried out using the Google Earth Engine platform. A database was generated with information on coffee leaf miner infestation, vegetation indices, and assessment times. The database was separated into training data and testing data. Nine machine learning algorithms were used, including Linear Discriminant Analysis, Random Forest, Support Vector Machine, k-nearest neighbors, and Logistic Regression, and a principal component analysis was conducted for each algorithm. After optimizing the hyperparameters, the testing data were used to validate the model. The best model to estimate miner infestation was RF, which had an accuracy of 0.86, a kappa index of 0.64, and a precision of 0.87. The developed models were capable of monitoring coffee leaf miner infestation. [ABSTRACT FROM AUTHOR]

Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings grown in different containers, based on multispectral images acquired by Unmanned Aerial Vehicles (UAV). The study was conducted in Santo Antônio do Amparo, Minas Gerais, Brazil. The coffee plants were imaged by UAV, and their height, crown diameter, and chlorophyll content were measured in the field. The vegetation indices were compared to the field measurements through graphical and correlation analysis. According to the results, no significant differences were found between the studied variables. However, the area transplanted with seedlings grown in perforated bags showed a lower percentage of mortality than the treatment with root trainers (6.4% vs. 11.7%). Additionally, the vegetation indices, including normalized difference red-edge, normalized difference vegetation index, and canopy planar area calculated by vectorization (cm2), were strongly correlated with biophysical parameters. Linear models were successfully developed to predict biophysical parameters, such as the leaf area index. Moreover, UAV proved to be an effective tool for monitoring coffee using this approach. [ABSTRACT FROM AUTHOR]

A form of rural tourism in expansion is horse tourism. Equitourism is a form of hiking that allows for an intimate and natural use of landscapes and local territories. Equestrian tourism today no longer represents a small elite market niche, as it has become a true form of pastime for an increasing number of users. Trail planning and management need to consider the changes that are already occurring, as well as those that are predicted to lie ahead. The key trends that are expected to have an impact on recreational horse trail planning and management are highlighted in this research. Many of these trends have been emerging over decades and have rapidly increased due to the impacts of the COVID-19 pandemic. In recent years, in many Italian regions, especially in Tuscany, thanks to the enhancement of the Tuscan equestrian routes, projects for the promotion of equestrian tourism have been developed. In this work the methodology applied was based on the identification of an appropriate database created in the GIS context and specifically coded for equestrian tourism. The aim is to guarantee the creation of an innovative and functional product to meet the needs of this sustainable tourism sector, providing information capable of facilitating the end user, who, through their GPS, georeferenced images and maps, will have the possibility of planning and traveling the horse trail in complete safety. The final result of this research (the first case study in Italy) is the creation of a modern and complete information database of a horse trail that can be managed using GIS. [ABSTRACT FROM AUTHOR]