Search

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]

Brazil is the largest producer and exporter of coffee beans in the world. Given this relevance, it is important to monitor the crop to prevent attacks by pests. This study aimed to detect leaf miner (Leucoptera coffeella) infestation in a newly planted crop based on vegetation indices (VI) derived from aerial images obtained by a multispectral camera embedded in a remotely piloted aircraft (RPA) using random forest (RF). The study was conducted on the Cafua farm in the municipality of Lavras in southern Minas Gerais. The images were collected using a multispectral camera attached to a remotely piloted aircraft (RPA). Collections were carried out on 30 July 2019 (infested crop) and 16 December 2019 (post chemical control). The RF package in R software was used to classify the infested and healthy plants. The t test revealed significant differences in band means between healthy and infested plants, favouring higher means in healthy plants. VI also exhibited significant differences, with EXR being higher in infested plants and GNDVI, GOSAVI, GRRI, MPRI, NDI, NDRE, NDVI and SAVI showing higher averages in healthy plants, indicating distinct spectral responses and light absorption patterns between the two states of the plant. Due to the spectral differences between the classes, it was possible to classify the infested and healthy plants, and the RF algorithm performed very well. [ABSTRACT FROM AUTHOR]

The use of new technologies to monitor and evaluate the management of coffee crops allowed for a significant increase in productivity. Precision coffee farming has leveraged the development of this commodity by using remote sensing and Unmanned Aerial Vehicles (UAVs). However, the success of coffee farming in the country also resulted from management practices, including liming management in the soils. This study aimed to evaluate the response of coffee seedlings transplanted to areas subjected to deep liming in comparison to conventional (surface) liming, using vegetation indices (VIs) generated by multispectral images acquired using UAVs. The study area was overflown bimonthly by UAVs to measure the plant height, crown diameter, and chlorophyll content in the field. The VIs were generated and compared with the data measured in the field using linear time graphs and a correlation analysis. Linear regression was performed to predict the biophysical parameters as a function of the VIs. A significant difference was found only in the chlorophyll content. Most indices were correlated with the biophysical parameters, particularly the green chlorophyll index (GCI) and the canopy area calculated via vectorization. Therefore, UAVs proved to be effective coffee monitoring tools and can be recommended for coffee producers. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is one important agrometeorological parameter for hydrological studies and climate risk zoning. ET0 calculation by the FAO Penman–Monteith method requires several input data. However, the availability of climate data has been a problem in many places around the world, so the study of scenarios with different combinations of climate data has become essential. The aim of this study was to evaluate the performance of artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR) approaches to estimate monthly mean ET0 with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I–SI), and at the regional level, where the Minas Gerais state was divided according to the climatic classifications of Thornthwaite (Scenario II–SII) and Köppen (Scenario III–SIII). ANN and RF performed better in ET0 estimation among the models evaluated in the SI, SII, and SIII scenarios with the following data combinations: (i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity and (ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. SVM and MLR models are recommended for all scenarios in situations with limited climatic data where only air temperature and relative humidity data are available. The results and information presented in this study are important for the agricultural chain and water resources in Minas Gerais state. [ABSTRACT FROM AUTHOR]

Viticulture began growing in Brazil in recent years, and this growth is due to the introduction of new techniques and improvements used in vine planting, especially the use of plastic covering in the vineyard. Thus, this study aimed to evaluate the economic viability of plastic covering installation, as well as the quality of grapes produced by two cultivars, in order to compare these results with those obtained by a conventional cultivation system. The experiment was performed at an experimental farm producing grapes in southern Brazil. The grape cultivars Niagara Branca and Niagara Rosada were studied and cultivated with and without plastic covering. Data related to the costs of vineyard planting, establishment, and seasonal management, in addition to the grape revenue, were assessed. Additionally, the grape yield and quality were evaluated. The analysis of return indicators showed that the net present value was higher for the plastic-covered system, which indicates greater profitability and consequent viability of the investment. Moreover, the grape quality varied according to the cultivar and production system. In this context, it is recommended to use the cultivar Niagara Rosada for production in a plastic covering system. [ABSTRACT FROM AUTHOR]

The System Operator (SO), also known as Nine Boxes or Multiscreen, is classically used for analysing the undesired situation according to different perspectives. However, its logic can be suitably exploited also for other kinds of tasks. In such a context, this paper shows an alternative application of the SO. In particular, the new proposed SO approach allows us to formulate alternative problems, i.e., different from the central one, although referring to the same problem-solving task. By applying the proposed procedure to a real case study, this paper shows that the tool can be used for problem analysis and formulation purposes, aimed at better clarifying the problem-solving task. The case study concerns a preliminary set of experiments performed to evaluate the possibility of obtaining angular shapes by bending tiles made by a specific compostable composite material. The suggestions obtained using the proposed SO approach allowed us to find potentially valid solutions, thus paving the way for further validation tests. In particular, tests are still needed to validate the actual potentialities of the proposed SO. Additionally, further and more comprehensive tests are required to validate the solutions inferred in this study concerning the compostable tiles. [ABSTRACT FROM AUTHOR]

Straw is an inevitable product of cereal production and is available in huge quantities in the world. In order to use straw-bales as a building material, the characteristic values of the thermal performances should be determined. To not lose the benefits of the cheapness and sustainability of the material, the characteristics must be determined with simple and inexpensive means and procedures. This research aims to implement tools and methods focused at the determination of the thermal properties of straw-bales. For this study, the guidelines dictated by ASTM and ISO were followed. A measurement system consisting of a Metering Chamber (MC) was realized. The MC was placed inside a Climate Chamber (CC). During the test, a known quantity of energy is introduced inside MC. When the steady-state is reached, all the energy put into MC passes through its walls in CC, where it is absorbed by the air-conditioner. A series of thermopiles detect the temperature of the surfaces of the measurement system and of the specimen. Determining the amount of energy transmitted by the various parts of MC and by the specimen, it is possible to apply Fourier's law to calculate the thermal conductivity of the specimen. [ABSTRACT FROM AUTHOR]

This research concerns the design of an agricultural building with a high degree of sustainability, located in a farm in the south of the Tuscany region, Italy. The building, intended mainly as a wine cellar, offers innovative construction solutions of high deconstructability and has features of low environmental impact, economic competitiveness and constructive simplicity. In particular, the design of the basement cellar involves the use of gabions and stones for the realization of the foundations, the ground retaining walls and all other bearing walls. A different solution is adopted for the external wall which remains entirely above ground. It is also made by gabions, but it is externally covered with a coat of straw bales and is plastered with clay or lime. The roof-bearing structure is made of steel beams and galvanized steel sheets. A layer of fertile soil is arranged on the roof to form a green roof system. This research aims to spread the design criteria of deconstructable buildings, based on the use of natural materials with low environmental and economic impact. Where it is not possible to employ natural materials, reusable or recyclable materials are used. [ABSTRACT FROM AUTHOR]

Terraced systems currently represent an indubitable added value for Tuscany, as well as for other Italian regions and for several Mediterranean countries. This value goes beyond their original function of hosting new areas for cultivation. The hydrological functions performed by these systems, including control of erosion, stabilisation of the slopes, prolongation of runoff times and the possible reduction of the volumes of surface runoff, are well-known. In addition, they also play a strategic role in the conservation of biodiversity and in maintaining local identity. At a national level, the terraced agricultural systems fall within the scope of actions scheduled in the National Strategic Plan for Rural Development 2007-2013, and the standards of Good Agricultural and Environmental Conditions (GAECs) envisages that they be maintained through the granting of economic aid as laid down in the Rural Development Plans 2007-2013 and 2014-2020. Eighteen sample areas, previously selected on the basis of the terracing intensity index (defined as the ratio between the lines representing the walls and the surface of 1 ha), were subjected for on-site surveys to determine the geo-typological features through the identification and measurement of the main technical-construction parameters of the dry stone walls. This analysis also enabled assessments of the overall state of conservation of the dry stone walls in order to suggest operations for safeguarding and protection. [ABSTRACT FROM AUTHOR]

Terraced areas have existed in Italy since ancient times, and they continue to be characteristic elements of the cultural identity of the country. The progressive abandonment of rural areas and farmland that began in the 1960s has led to the disintegration and disappearance of many terraces, representing one of the problems connected with the deterioration of the historic Tuscan agricultural landscape. This research aims to provide a contribution to the territorial analysis of the agricultural terraced landscapes at a regional scale. The preliminary phase of the study involved setting up a working method in Geographic Information System (GIS) for the quantitative definition of the population. Afterwards, for the territorial analysis at a more detailed scale, a method was devised to identify the areas of greater significance in terms of a terracing intensity index. The final results concerned considerations on the distribution of the terraced landscapes and analyses related to land use and the main environmental parameters of the most representative terraced systems. [ABSTRACT FROM AUTHOR]

Brazil is the main producer and exporter and the second-largest consumer of coffee in the world, and Remotely Piloted Aircraft Systems stands out as an efficient remote detection technique applied to the study and mapping of crops. The objective of this study was to characterize three recently planted cultivars of Coffea arabica L. The study area is in Minas Gerais, Brazil, with a coffee plantation of the initial age of 5 months. The temporal behavior was determined based on monthly mean values. The spectral profile was obtained with mean values of the last month of dry and rainy periods. The statistical differences were obtained based on the non-parametric test of multiple comparisons. The estimation of the exponential equation was obtained through the Spearman correlation coefficient of determination and root mean square error. It was concluded that the seasons influence the behavior and development of cultivars, and significant statistical differences were detected for the variables, except for the chlorophyll variable. Due to the proximity and overlap of the reflectance values, spectral bands were not used to individualize cultivars. A correlation between the vegetation indices and leaf area index was observed and the exponential regression equation was estimated for each cultivar under study. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R2 values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way. [ABSTRACT FROM AUTHOR]

Mechanized operations on terrain slopes can still lead to considerable errors in the alignment and distribution of plants. Knowing slope interference in semi-mechanized planting quality can contribute to precision improvement in decision making, mainly in regions with high slope. This study evaluates the quality of semi-mechanized coffee planting in different land slopes using a remotely piloted aircraft (RPA) and statistical process control (SPC). In a commercial coffee plantation, aerial images were collected by a remotely piloted aircraft (RPA) and subsequently transformed into a digital elevation model (DEM) and a slope map. Slope data were subjected to variance analysis and statistical process control (SPC). Dependent variables analyzed were variations in distance between planting lines and between plants in line. The distribution of plants on all the slopes evaluated was below expected; the most impacted was the slope between 20–25%, implementing 7.8% fewer plants than projected. Inferences about the spacing between plants in the planting row showed that in slopes between 30–40%, the spacing was 0.53 m and between 0 and 15% was 0.55 m. This denotes the compensation of the speed of the operation on different slopes. The spacing between the planting lines had unusual variations on steep slopes. The SCP quality graphics are of lower quality in operations between 30–40%, as they have an average spacing of 3.65 m and discrepant points in the graphics. Spacing variations were observed in all slopes as shown in the SCP charts, and possible causes and implications for future management were discussed, contributing to improvements in the culture installation stage. [ABSTRACT FROM AUTHOR]

The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer. [ABSTRACT FROM AUTHOR]