In this research, a model for the estimation of antioxidant content in cherry fruits from multispectral imagery acquired from drones was developed, based on machine learning methods. For two consecutive cultivation years, the trees were sampled on different dates and then analysed for their fruits' radical scavenging activity (DPPH) and Folin-Ciocalteu (FCR) reducing capacity. Multispectral images from unmanned aerial vehicles were acquired on the same dates with fruit sampling. Soil samples were collected throughout the study fields at the end of the season. Topographic, hydrographic and weather data also were included in modelling. First-year data were used for model-fitting, whereas second-year data for testing. Spatial autocorrelation tests indicated unbiased sampling and, moreover, allowed restriction of modelling input parameters to a smaller group. The optimum model employs 24 input variables resulting in a 6.74 root mean square error. Provided that soil profiles and other ancillary data are known in advance of the cultivation season, capturing drone images in critical growth phases, together with contemporary weather data, can support site-and time-specific harvesting. It could also support site-specific treatments (precision farming) for improving fruit quality in the long-term, with analogous marketing perspectives.
SUMMARYThe determination of time for grape harvest is probably the most important decision for wine making producers, because grapes are not climacteric fruits and if they are harvested before fully ripe their quality is compromised. This is because sugar content, aroma and color compounds increase only before harvest for non-climacteric fruits. The current practice for determining berry ripeness includes measurements of berry samples for total soluble solids (TSS) and pH, but this procedure is time consuming and laborious. On the other hand, with the development of unmanned aerial vehicles (UAV) and modern ultralight cameras the grower can now obtain data rapidly and also spatial information for crop's physiological status at farm scale. Berry samples were collected from grapevines (cv. Malagousia) and their reflectance spectra were used to estimate TSS and pH by Multiple Linear Regression (MLR) and Support Vector Machine (SVM). The highest classification accuracy was achieved using the SVM model. Moreover, berries taken by grapevines with low Carotenoid Reflectance Index 2 (CRI2) had higher TSS, pH and terpenes, and gave wine with higher alcohol by volume. The importance for constructing a model for predicting TSS in berries is obvious, because this can aid in the prediction of wine quality. The current work is a preliminary compilation of methodologies for making a monitoring tool of berry ripeness, using statistical techniques, remote sensing and crop physiological data. RESUMOA determinação da época de vindima é provavelmente a decisão mais importante para os produtores vinícolas porque as uvas não são frutos climatéricos e se forem vindimadas antes de estarem completamente maduras, a sua qualidade fica comprometida. Isto ocorre porque o teor de açúcar, assim como os compostos responsáveis pelo aroma e pela cor aumentam apenas antes da colheita de frutos não climatéricos. A prática atual para determinar a maturação da uva inclui medições de amostras de uva quanto a sólidos solúveis totais (TSS) e pH, mas este procedimento é demorado e trabalhoso. Por outro lado, com o desenvolvimento de veículos aéreos automatizados (UAV) e câmaras ultraleves modernas, o produtor pode agora obter dados rapidamente e também informação espacial sobre o estado fisiológico da cultura à escala da propriedade. Foram recolhidas amostras de uvas de videiras (cv. Malagousia) e os seus espetros de reflexão foram utilizados para estimar o TSS e o pH através de Regressão Linear Múltipla (MLR) e de uma Máquina de Vetores de Suporte (SVM). A precisão da classificação mais alta foi obtida com a utilização do modelo SVM. Além disso, as uvas de videiras com baixo Índice de Reflexão de Carotenoide 2 (CRI2) alcançaram valores de TSS, pH e terpenos mais elevados, dando origem a vinho com mais álcool por volume. A importância para a construção de um modelo de previsão de TSS em uvas é óbvia, porque pode auxiliar na previsão da qualidade do vinho. O presente trabalho é uma compilação preliminar de metodologias para criar uma ferramenta de monito...
In the field of plant disease protection many approaches exist, but all acknowledge the necessity of fast and accurate identification of the source in order to make the most efficient applications. In this work, a remote sensing approach for the detection of Verticillium dahliae in olive fields is presented. A model was created that calculates a spectral index based on Sentinel-2 data and uses it to assess the levels of stress in olive trees in the region of Chalkidiki, Greece. The derived map provides an overview of the situation concerning stress levels in olive fields across a large area and in a small amount of time. Additionally, because of the constant flow of data from the Sentinel satellites, a time series of calculations can be used as an anomaly indicator for regions of interest. This model can be implemented in an e-Infrastructure as a cloud service to further enhance its usability by relevant parties, such as agricultural advisors and scientists.
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