ABSTRACT – Volatile compounds, quality traits (total phenols and antioxidant capacity) and High-performance liquid chromatography (HPLC)-isolated polyphenols of strawberries, variety Sabrosa, commercially referred to as “Candonga”, harvested at three different times (H1, H2 and H3) and at two different ripening stages, namely half-red (Half-red-H1, Half-red-H2 and Half-red-H3) and red (Red-H1, Red-H2 and Red-H3) were evaluated. Dominant anthocyanins, namely cyanidin-3-O-glucoside, pelargonidin-3-O-glucoside and pelargonidin-3-O-rutinoside, as well as p-coumaryl hexoside increased during harvesting, differently from flavonoids, such as quercetin-3-O-glucoside, kaempferol-3-O-glucoronide and quercetin 3-O-glucoronide, that declined. Samples clustered in different quadrants of the principal component analysis (PCA) performed on volatiles, quality traits and phenolic compounds, highlighting that only the red samples were directly correlated to volatile components, as volatiles clearly increased both in number and amount during ripening. In particular, volatiles with a positive impact on the consumers’ acceptance, including butyl butyrate, ethyl hexanoate, hexyl acetate, nonanal, terpenes and lactones, were positively associated with the Red-H1 and Red-H2 strawberries, while volatiles with negative coefficients related to consumer liking, including isopropyl butyrate, isoamyl butyrate and mesifurane directly correlated with the Red-H3 samples. Accordingly, strawberries harvested at Red-H1 and Red-H2 ripening stages could be preferred by the consumers compared to the Red-H3 fruit. Altogether, these results could help to individuate quality traits as putative markers of the ripening stage, and optimize the process of post-harvesting ripening to preserve or improve the desirable aromatic characteristics of strawberries.
ABSTRACT – Vineyard classification is an important process within viticulture-related decision-support systems. Indeed, it improves grapevine vegetation detection, enabling both the assessment of vineyard vegetative properties and the optimization of in-field management tasks. Aerial data acquired by sensors coupled to unmanned aerial vehicles (UAVs) may be used to achieve it. Flight campaigns were conducted to acquire both RGB and multispectral data from three vineyards located in Portugal and in Italy. Red, green, blue and near infrared orthorectified mosaics resulted from the photogrammetric processing of the acquired data. They were then used to calculate RGB and multispectral vegetation indices, as well as a crop surface model (CSM). Three different supervised machine learning (ML) approaches—support vector machine (SVM), random forest (RF) and artificial neural network (ANN)—were trained to classify elements present within each vineyard into one of four classes: grapevine, shadow, soil and other vegetation. The trained models were then used to classify vineyards objects, generated from an object-based image analysis (OBIA) approach, into the four classes. Classification outcomes were compared with an automatic point-cloud classification approach and threshold-based approaches. Results shown that ANN provided a better overall classification performance, regardless of the type of features used. Features based on RGB data showed better performance than the ones based only on multispectral data. However, a higher performance was achieved when using features from both sensors. The methods presented in this study that resort to data acquired from different sensors are suitable to be used in the vineyard classification process. Furthermore, they also may be applied in other land use classification scenarios.
ABSTRACT – An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar (cv) Golden Delicious infected by Alternaria alternata, one of the main pathogens responsible for MC disease. Apples were sampled in vertical and horizontal positions during five measurement rounds in 13 days’ time, and 700 spectral signatures were collected. Spectral correlation together with transmittance temporal patterns and ANOVA showed that the spectral region from 863.38 to 877.69 nm was most linked to MC presence. Then, two binary classification models based on Artificial Neural Network Pattern Recognition (ANN-AP) and Bagging Classifier (BC) with decision trees were developed, revealing a better detection capability by ANN-AP, especially in the early stage of infection, where the predictive accuracy was 100% at round 1 and 97.15% at round 2. In subsequent rounds, the classification results were similar in ANN-AP and BC models. The system proposed surpassed previous MC detection methods, needing only one measurement per fruit, while further research is needed to extend it to different cultivars or fruits.
ABSTRACT – Automatic yield monitoring and in-field robotic harvesting by low-cost cameras require object detection and segmentation solutions to tackle the poor quality of natural images and the lack of exactly-labeled datasets of consistent sizes. This work proposed the application of deep learning for semantic segmentation of natural images acquired by a low-cost RGB-D camera in a commercial vineyard. Several deep architectures were trained and compared on 85 labeled images. Three semi-supervised learning methods (PseudoLabeling, Distillation and Model Distillation) were proposed to take advantage of 320 non-annotated images. In these experiments, the DeepLabV3+ architecture with a ResNext50 backbone, trained with the set of labeled images, achieved the best overall accuracy of 84.78%. In contrast, the Manet architecture combined with the EfficientnetB3 backbone reached the highest accuracy for the bunch class (85.69%). The application of semi-supervised learning methods boosted the segmentation accuracy between 5.62 and 6.01%, on average. Further discussions are presented to show the effects of a fine-grained manual image annotation on the accuracy of the proposed methods and to compare time requirements.
ABSTRACT – Automated in-field data gathering is essential for crop monitoring and management and for precision farming treatments. To this end, consumer-grade digital cameras have been shown to offer a flexible and affordable sensing solution. This paper describes the integration and development of a cost-effective multi-view RGB-D device for sensing and modeling of agricultural environments. The system features three RGB-D sensors, arranged to cover a horizontal field of view of about 130 deg in front of the vehicle, and a suite of localization sensors consisting of a tracking camera, an RTK-GPS sensor and an IMU device. The system is intended to be mounted on-board an agricultural vehicle to provide multi-channel information of the surveyed scene including color, infrared and depth images, which are then combined with localization data to build a multi-view 3D geo-referenced map of the traversed crop. The experimental demonstrator of the multi-sensor system is presented along with the steps for the integration of the different sensor data into a unique multi-view map. Results of field experiments conducted in a commercial vineyard are included, as well, showing the effectiveness of the proposed system. The resulting map could be useful for precision agriculture applications, including crop health monitoring, and to support autonomous driving.
ABSTRACT – Olea europaea L. is a glycophyte representing one of the most important plants in the Mediterranean area, both from an economic and agricultural point of view. Its adaptability to different environmental conditions enables its cultivation in numerous agricultural scenarios, even on marginal areas, characterized by soils unsuitable for other crops. Salt stress represents one current major threats to crop production, including olive tree. In order to overcome this constraint, several cultivars have been evaluated over the years using biochemical and physiological methods to select the most suitable ones for cultivation in harsh environments. Thus the development of novel methodologies have provided useful tools for evaluating the adaptive capacity of cultivars, among which the evaluation of the plant-microbiota ratio, which is important for the maintenance of plant homeostasis. In the present study, four olive tree cultivars (two traditional and two for intensive cultivation) were subjected to saline stress using two concentrations of salt, 100 mM and 200 mM. The effects of stress on diverse cultivars were assessed by using biochemical analyses (i.e., proline, carotenoid and chlorophyll content), showing a cultivar-dependent response. Additionally, the olive tree response to stress was correlated with the leaf endophytic bacterial community. Results of the metabarcoding analyses showed a significant shift in the resident microbiome for plants subjected to moderate salt stress, which did not occur under extreme salt-stress conditions. In the whole, these results showed that the integration of stress markers and endophytic community represents a suitable approach to evaluate the adaptation of cultivars to environmental stresses.
ABSTRACT – Traditionally, strawberries are harvested manually when the typical colour of the cultivar does not reach at least 80% of the surface. The focus of this research activity is to develop an automatic system based on image analysis in order to objectively define the optimal harvest time. Strawberries (cv. Sabrosa), with different degrees of maturation, were analyzed in four different harvesting periods and subsequently selected and classified, based on the ripening percentage, in three maturity classes: R025, R5070 and R75100. Each class of 10 strawberries, evaluated in triplicate, was subjected to image analysis and physiological and qualitative evaluation by measuring the following parameters: respiratory activity, pH, total soluble solids content, and titratable acidity. The images, captured by a digital camera, were processed using Matlab® software and all the data found were supported by multivariate analysis. The image processing has made it possible to create an algorithm measuring objectively the percentage and the saturation level of red assigning the fruit to each Class. Principal component analysis (PCA) shows that discriminating parameters are the Chroma and the red Area, then used in a Partial LastSquare Regression (PLSR) model to predict the TSS/TA ratio with R2 of 0.7 and 0.6 for calibration and validation set, respectively.