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Given the systematic acquisition of satellite data, it is possible to generate up-to-date land cover (LC) maps, essential for effective agricultural territory management, environmental monitoring, and informed decision-making. Typically, creating a LC map requires collecting high-quality labeled data, a process that is both costly and time-consuming. To mitigate the need to collect large volume of labeled data, we propose a deep learning framework called REFeD (data Reuse with Effective Feature Disentanglement for land cover mapping), which leverages already available out-of-year reference data to enhance the production of up-to-date LC maps. To this end, REFeD integrates remote sensing and reference data from different domains (e.g., historical and recent data) utilizing a disentanglement strategy based on contrastive learning. By separating domain-invariant and domain-specific features, REFeD isolates useful information associated to the downstream LC mapping task and mitigates distribution shifts between domains. Moreover, REFeD incorporates an effective supervision scheme to reinforce feature disentanglement through multiple levels of supervision at different granularities. Experimental evaluation on study areas characterized by diverse landscapes, including Koumbia (West Africa, Burkina Faso) and Centre-Val de Loire (central Europe, France), demonstrates the effectiveness of the proposed approach.

A stuck leaflet is a rare complication of transcatheter aortic valve implantation (TAVI). It occurs when one of the leaflets in the implanted valve doesn’t open and close properly. This complication can lead to serious health problems, even death.

Landform mapping is the initial step of many geomorphological analyses (e.g. assessment of natural hazards and natural resources) and requires vast resources to be applied to wide areas at high-resolution. Among geomorphological objects, we focus on glacial moraine mapping, since it is a task relevant to many fields (e.g. paleoclimate and glacial geomorphology). Here we proposed to exploit the potential of Deep Learning-based approaches to map moraine landforms by exploiting multi-source remote sensing imagery. To this end, we propose the first Deep Learning model to map glacial moraines, namely MorNet. As multi-source remote sensing information, we combine together three different sources: Topographic (Pleiades-derived DSM), Multispectral (Sentinel-2), and SAR (Sentinel-1) data. To cope with such heterogeneous information, the proposed model has a dedicated branch for each input source and, a late fusion mechanism is leveraged to combine them with the aim to provide the final mapping. The performance of the MorNet model is evaluated on several glacier valleys in China in the Himalayan range. This area contains minimally eroded moraines, so they are well-defined and of varied morphology. The behavior of the proposed method is compared to models using individual mono-source models in order to highlight the benefit to simultaneously leverage multi-source information. The use of multi-source data allows MorNet to exploit the complementarity of the three input sources and improve its performance from an f1-score of about 41.6 using a single source to 52.8 using three sources. MorNet provides a first-order moraine map through its ability to identify well-defined moraines. Consequently, MorNet can identify areas likely to contain moraines and intends to be used as a tool by experts to facilitate and support large-scale mapping.

Encarsia protransvena (Hymenoptera: Aphelinidae) is recorded here for the first time in Italy as a parasitoid of the whitefly Singhiella simplex (Hemiptera: Aleyrodidae), one of the most invasive alien pests of exotic Ficus species. Singhiella simplex, originating from the Oriental region, has established a global presence. Monitoring of the whitefly and its parasitoids was conducted in the southern areas of Italy, providing crucial insights into their distribution and interactions. The taxonomic identity of E. protransvena, was confirmed by scrutiny of morphological and molecular taxonomic characters. At the time of writing, we also obtained some specimens of Encarsia hispida from the host collected in Campania. We reserve the right to provide bio-ethological information on the species in the future. Comprehensive illustrations and diagnostic features are provided for the host and the parasitoids. An identification key is included for all Encarsia species associated with S. simplex, which provides a valuable tool to distinguish these aphelinid wasps for future research and applications in biological control programs.

Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize).

Positive and unlabelled (PU) learning for multi-variate time series classification refers to build a binary classification model when only a small set of positive and a large set of unlabelled samples are accessible at training stage. Different from binary semi-supervised scenario in which the training set contains labelled samples from both positive and negative classes, in the PU learning setting, only positive samples are labelled due to cost-restriction or issues related to defining what belongs to the negative class. With the objective to deal with this challenging task, here, we propose a new deep learning framework, referred as DMTS-PUL. Our method has two different steps: firstly, it selects a set of reliable negative samples from the set of unlabelled data and, successively, it iteratively enriches the training data by selecting pseudo-labels to train a binary classification model via self-training. Experimental evaluations on several benchmarks have highlighted the quality of DMTS-PUL w.r.t. competing approaches and the obtained findings have pointed out the suitability of our proposal when only small amounts of positive labelled samples are available.

Sensitivity of lidar metrics to scan angle can affect the robustness of area-based approach (ABA) models, and modelling the interplay of scan geometry and terrain properties can be complex. The study hypothesizes that neural networks can manage the interplay of lidar acquisition parameters, terrain properties, and vegetation characteristics to improve ABA models. The study area is in Massif des Bauges Natural Regional Park, eastern France, comprising 291 field plots in a mountainous environment with broadleaf, coniferous, and mixed forest types. Field plots were scanned with a high overlap from multiple flight lines and the corresponding point clouds were considered independently to expand the standard ABA dataset (291 observations) to create a dataset containing 1095 independent observations. Computation of lidar, terrain, and scan angle metrics for each point cloud associated each observation in the expanded dataset with the scan information in addition to the lidar and terrain information. A multilayer perceptron (MLP) was used to model basal area and total volume to compare the predictions resulting from standard and expanded ABA datasets. With expanded datasets containing lidar, terrain, and scan information, the R2 for the median predictions per plot were higher (R2 of 0.83 and 0.85 for BA and Vtot) than predictions with standard datasets (R2 of 0.66(BA) and 0.71(Vtot)) containing only lidar metrics. It also outperformed an MLP model for a dataset with lidar and terrain information [R2 of 0.77(BA and Vtot)]. The MLP performed better than Random forest regression, which could not sufficiently exploit additional terrain and scan information.

This study aims to enhance rapeseed field detection accuracy using Sentinel-1 (S1) time series data and addressing challenges in collecting ground samples. The proposed solutions include model transfer between years without retraining and secondly, developing models with limited training samples. The research evaluates the performance of Random Forest (RF) and three deep learning (DL) algorithms: Long Short-Term Memory Fully Convolutional Network (LSTM-FCN), InceptionTime, and Multi-layer Perceptron (MLP). All four algorithms are evaluated initially with abundant ground samples and later with smaller sample sizes (100, 300, 500 and 1000 samples). Model transferability is tested across years. The impact of S1 image count on transfer accuracy is examined. Additionally, the effect of the phenological shift in the rapeseed growth cycle of 15 and 30 days between the training and test years was also investigated. The findings demonstrate strong model performance when training and testing occur in the same year (F1-score up to 95%). Within sample sizes of 300 to 1000, RF and InceptionTime stand out with high accuracy (F1-score>90%). When employing different years for training and testing with abundant sample sizes, all four algorithms correctly classified rapeseed (F1-score between 85.5% and 92.7%). In cases of a reduced number of images, the performance of InceptionTime and LSTM-FCN decreased (16% decrease in the F1-score), while RF and MLP maintain their performance. Notably, RF outperforms DL algorithms with an F1-score of 89.1%. In the context of a phenological shift, only InceptionTime and LSTM-FCN demonstrated strong performance (F1-score between 87.7% and 92.6%).

Nowadays, satellite image time series (SITS) are commonly employed to derive land-cover maps (LCM) to support decision makers in a variety of land management applications. In the most general workflow, the production of LCM strongly relies on available GT data to train supervised machine learning models. Unfortunately, these data are not always available due to time-consuming and costly field campaigns. In this scenario, the possibility to transfer a model learnt on a particular year (source domain) to a successive period of time (target domain), over the same study area, can save time and money. Such a kind of model transfer is challenging due to different acquisition conditions affecting each time period, thus resulting in possible distribution shifts between source and target domains. In the general field of machine learning, unsupervised domain adaptation (UDA) approaches are well suited to cope with the learning of models under distribution shifts between source and target domains. While widely explored in the general computer vision field, they are still underinvestigated for SITS-based land-cover mapping, especially for the temporal transfer scenario. With the aim to cope with this scenario in the context of SITS-based land-cover mapping, here we propose spatially aligned domain-adversarial neural network, a framework that combines both adversarial learning and self-training to transfer a classification model from a time period (year) to a successive one on a specific study area. Experimental assessment on a study area located in Burkina Faso characterized by challenging operational constraints demonstrates the significance of our proposal. The obtained results have shown that our proposal outperforms all the UDA competing methods by 7 to 12 points of F1-score across three different transfer tasks.

Up-to-date canopy height model (CHM) estimates are of key importance for forest resource monitoring and disturbance analysis. In this article, we present a study on the potential of deep learning (DL) for the regression of forest height from TanDEM-X bistatic interferometric synthetic aperture radar (InSAR) data. We propose a novel fully convolutional neural network framework, trained in a supervised manner using reference CHM measurements derived from the LiDAR LVIS airborne sensor from NASA. The reference measurements were acquired during the joint NASA–ESA 2016 AfriSAR campaign over five sites in Gabon, Africa, characterized by the presence of different kinds of vegetation, spanning from tropical primary forests to mangroves. Together with the DL architecture and training strategy, we present a series of experiments to assess the impact of different input features on the network estimation accuracy (in particular of bistatic InSAR-related ones). When tested on all the considered sites, the proposed DL model achieves an overall performance of 1.46-m mean error, 4.2-m mean absolute error, and 15.06% mean absolute percentage error. Furthermore, we perform a spatial transfer analysis aimed at deriving preliminary insights on the generalization capability of the network when trained and tested on datasets acquired over different locations, combining different kinds of tropical vegetation. The obtained results are promising and already in line with state-of-the-art methods based on both physical-based modeling and data-driven approaches, with the remarkable advantage of requiring only one single TanDEM-X acquisition at inference time.

Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 reanalysis dataset. Resampling the time series over thermal time significantly enhances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2. Finally, we propose a new two-stage machine learning strategy to mitigate early season partially available data. It consists in extending the current time series on the basis of complete historical data and adapting the model inference according to the crop progress.

Abstract Background On December 2017 the Italian Parliament approved law n. 219/2017 “Provisions for informed consent and advance directives” regarding challenging legal and bioethical issues related to healthcare decisions and end-of-life choices. The law does not contain an explicit reference to Ethics Committees (ECs), but they could still play a role in implementing the law. Methods A questionnaire-based survey was performed among the ECs of the Italian Institute for Research and Care belonging to the Network of neuroscience and neurorehabilitation, with the aim of (1) knowing whether the ECs participated and, if so, how in the process of implementation of law n. 219/2017 in the referring institutes; (2) investigating the point of view of the ECs regarding their possible involvement in the process; (3) exploring the contribution ECs can provide to give effective implementation to the law principles and provisions. Results Seventeen ECs out of thirty took part in the survey; the characteristics of the responding and non-responding committees are similar, so the responding ECs can be regarded as representative of all ECs in the Network. Nine ECs did not discuss the law in anyway: the main reason for this is that the referring institutions (6) and the health care professionals (3) did not ask for an EC intervention. Nevertheless, the large majority of the ECs believe that their involvement in the implementation of the law as a whole is appropriate (8) or absolutely appropriate (6), while 3 of them are neutral. No EC believes that the involvement is inappropriate. The aspect of the law on which the 14 ECs converge in considering the EC involvement appropriate/absolutely appropriate is the one related to the health facilities obligation to guarantee the full and proper implementation of the principles of the law. Conclusions Our survey confirms that ECs believe they can play a role in the implementation of law n. 219/2017, although this does not entirely correspond to what the committees have actually done in reality. This role could be better exercised by ECs specifically established for clinical practice, which would have a composition, functioning and a mandate better suited to the purpose. This supports the call for a national regulation of ECs for clinical practice.

Simple Summary: We report the detection of Encarsia protransvena and confirm Encarsia hispida as parasitoids of the exotic invasive whitefly Singhiella simplex (Hemiptera: Aleyrodidae) in Italy. Native to the Oriental region, S. simplex has now spread worldwide, causing significant damage to exotic fig plants in southern Italy, providing a unique opportunity to study its natural interactions. The whitefly and its parasitoids were collected in some south Italian regions, obtaining two species of Chalcidoidea Aphelinidae. Using a combination of morphological and molecular analyses, we identified E. protransvena, a species reported as the only known natural enemy in the Mediterranean region. This discovery increases our understanding of the biology of both the pest and its parasitoid, offering valuable insights for future biological control programs. As we write this note, we also have obtained specimens of Encarsia hispida from the host collected in Campania. We aim to provide bio-ethological information about the species in the future. Encarsia protransvena (Hymenoptera: Aphelinidae) is recorded here for the first time in Italy as a parasitoid of the whitefly Singhiella simplex (Hemiptera: Aleyrodidae), one of the most invasive alien pests of exotic Ficus species. Singhiella simplex, originating from the Oriental region, has established a global presence. Monitoring of the whitefly and its parasitoids was conducted in the southern areas of Italy, providing crucial insights into their distribution and interactions. The taxonomic identity of E. protransvena, was confirmed by scrutiny of morphological and molecular taxonomic characters. At the time of writing, we also obtained some specimens of Encarsia hispida from the host collected in Campania. We reserve the right to provide bio-ethological information on the species in the future. Comprehensive illustrations and diagnostic features are provided for the host and the parasitoids. An identification key is included for all Encarsia species associated with S. simplex, which provides a valuable tool to distinguish these aphelinid wasps for future research and applications in biological control programs. [ABSTRACT FROM AUTHOR]

Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize). [ABSTRACT FROM AUTHOR]

Landform mapping is the initial step of many geomorphological analyses (e.g. assessment of natural hazards and natural resources) and requires vast resources to be applied to wide areas at high-resolution. Among geomorphological objects, we focus on glacial moraine mapping, since it is a task relevant to many fields (e.g. paleoclimate and glacial geomorphology). Here we proposed to exploit the potential of Deep Learning-based approaches to map moraine landforms by exploiting multi-source remote sensing imagery. To this end, we propose the first Deep Learning model to map glacial moraines, namely MorNet. As multi-source remote sensing information, we combine together three different sources: Topographic (Pleiades-derived DSM), Multispectral (Sentinel-2), and SAR (Sentinel-1) data. To cope with such heterogeneous information, the proposed model has a dedicated branch for each input source and, a late fusion mechanism is leveraged to combine them with the aim to provide the final mapping. The performance of the MorNet model is evaluated on several glacier valleys in China in the Himalayan range. This area contains minimally eroded moraines, so they are well-defined and of varied morphology. The behavior of the proposed method is compared to models using individual mono-source models in order to highlight the benefit to simultaneously leverage multi-source information. The use of multi-source data allows MorNet to exploit the complementarity of the three input sources and improve its performance from an f1-score of about 41.6 using a single source to 52.8 using three sources. MorNet provides a first-order moraine map through its ability to identify well-defined moraines. Consequently, MorNet can identify areas likely to contain moraines and intends to be used as a tool by experts to facilitate and support large-scale mapping. [ABSTRACT FROM AUTHOR]

This study investigates the potential of Sentinel-1 (S1) multi-temporal data for the early-season mapping of the rapeseed crop. Additionally, we explore the effectiveness of limiting the portion of a considered time series to map rapeseed fields. To this end, we conducted a quantitative analysis to assess several temporal windows (periods) spanning different phases of the rapeseed phenological cycle in the following two scenarios relating to the availability or constraints of providing ground samples for different years: (i) involving the same year for both training and the test, assuming the availability of ground samples for each year; and (ii) evaluating the temporal transferability of the classifier, considering the constraints of ground sampling. We employed two different classification methods that are renowned for their high performance in land cover mapping: the widely adopted random forest (RF) approach and a deep learning-based convolutional neural network, specifically the InceptionTime algorithm. To assess the classification outcomes, four evaluation metrics (recall, precision, F1 score, and Kappa) were employed. Using S1 time series data covering the entire rapeseed growth cycle, the tested algorithms achieved F1 scores close to 95% on same-year training and testing, and 92.0% when different years were used, both algorithms demonstrated robust performance. For early rapeseed detection within a two-month window post-sowing, RF and InceptionTime achieved F1 scores of 67.5% and 77.2%, respectively, and 79.8% and 88.9% when extended to six months. However, in the context of temporal transferability, both classifiers exhibited mean F1 scores below 50%. Notably, a 5-month time series, covering key growth stages such as stem elongation, inflorescence emergence, and fruit development, yielded a mean F1 score close to 95% for both algorithms when trained and tested in the same year. In the temporal transferability scenario, RF and InceptionTime achieved mean F1 scores of 92.0% and 90.0%, respectively, using a 5-month time series. Our findings underscore the importance of a concise S1 time series for effective rapeseed mapping, offering advantages in data storage and processing time. Overall, the study establishes the robustness of RF and InceptionTime in rapeseed detection scenarios, providing valuable insights for agricultural applications.

Deep learning-based land cover classifiers learnt from Satellite Image Time Series (SITS) are known to reach high performances. In order to explain, at least partly, the rationale leading to each one of their decisions, attention-based architectures have been proposed to automatically weight the importance of predefined data components in the classification process. Though generated for each decision separately, the informational content conveyed by such explanations can remain insufficient to end-users because of the complex nature of SITS. Moreover, getting a general perspective about the way a classifier works requires merging all explanations for each class and relating them to its mode of operation, which is not always straightforward. A preliminary and complementary approach for automatically identifying the data features detected by a pixel-wise deep spatiotemporal land cover classifier and explaining its behavior at the class level is therefore proposed in this paper. Classified pixels are first described using interpretable features coming under the form of data mining patterns. A redescription mining technique is then employed to automatically select, for each class, the features matching the different activation level configurations of the layer that is assumed to capture the aforementioned patterns. Experiments based on a Sentinel-2 time series and a deep spatiotemporal neural network implementing a channel-separated processing as well as a channel-based attention mechanism show the interest of such a combined approach.

Standard supervised classification methods make the assumption that the training data is fully annotated thus requiring an a-priory labelling process which is both costly and time-consuming. To relax this requirement, many different flavors of weakly supervised learning have been proposed. Among weakly supervised learning strategies, Positive Unlabelled learning (PUL) is gaining attention from the research community due to the wide spectrum of applications it can fit. However, the majority of research studies related to PUL only consider binary classification tasks while real-world applications commonly involve multiple categories. To deal with this limitation, Multi-Positive Unlabelled learning (MPUL) has been recently introduced to learn from examples labelled with multiple positive labels and a single unknown negative label. Up to today, only a limited number of research works were proposed to cope with this more general setting. In this paper, we propose a new MPUL framework based on deep learning strategies. Our framework, named ProtoMPUL (Prototype based Multi-Positive and Unlabelled Learning), combines metric learning and clustering strategies to model the set of positive classes as well as to characterize the unknown negative one. Experimental evaluations on real-world benchmarks considering recent MPUL competitors demonstrates that the proposed framework achieves state-of-the-art performances, thus supporting the validity of the proposed approach.

Forest ecosystems play a fundamental role in natural balances and climate mechanisms through their contribution to global carbon storage. Their sustainable management and conservation is crucial in the current context of global warming and biodiversity conservation. To tackle such challenges, earth observation data have been identified as a valuable source of information. While earth observation data constitute an unprecedented opportunity to monitor forest ecosystems, its effective exploitation still poses serious challenges since multimodal information needs to be combined to describe complex natural phenomena. To deal with this particular issue in the context of structure and biophysical variables estimation for forest characterization, we propose a new deep learning-based fusion strategy to combine together high density three-dimensional (3-D) point clouds acquired by airborne laser scanning with high-resolution optical imagery. In order to manage and fully exploit the available multimodal information, we implement a two-branch late fusion deep learning architecture taking advantage of the specificity of each modality. On the one hand, a 2-D CNN branch is devoted to the analysis of Sentinel-2 time series data, and on the other hand, a multilayer perceptron branch is dedicated to the processing of LiDAR-derived information. The performance of our framework is evaluated on two forest variables of interest: total volume and basal area at stand level. The obtained results underline that the availability of multimodal remote sensing data is not a direct synonym of performance improvements but, the way in which they are combined together is of paramount importance.

Welfare assessment of dairy calves is commonly conducted through the observation of their behavior and the analysis of physiological parameters. Despite the large number of studies on this topic, there is a lack of research on the possible correlation between the physiological parameters and behavior at basal level in dairy calves. For this reason, the present study aimed to investigate the possible correlations between serum cortisol, neutrophil-to-lymphocyte ratio (N/L) and locomotor play behavior in 21 Friesian dairy calves within their first month of life. Every week, an arena test after blood collection was performed. A significant positive correlation between serum cortisol levels and N/L (rrm = 0.381; 95% CI = 0.083, 0.617; p = 0.012) and a negative correlation between time spent running and N/L (rrm = −0.351; 95% CI = −0.575, −0.077; p = 0.012) were found. On the contrary, no significant correlation between time spent running and cortisol levels was detected. Cortisol levels and N/L were higher in the newborn and had a constant decrease until reaching a stable value after the 24th day of life. These findings suggest a better predictivity of N/L, compared to serum cortisol, on locomotor play behavior.

(1) Background: Since 2013, the pathogenic bacterium Xylella fastidiosa has been severely affecting olive production in Apulia, Italy, with consequences for the economy, local culture, landscape and biodiversity. The production of a phenolic extract from fresh olive leaves was employed for endotherapeutic injection into naturally infected olive trees by Xylella fastidiosa in Apulia region, Italy. (2) Methods: The effectiveness of the extract was tested in vitro and in planta in comparison with analogous treatments based on garlic powder and potassium phosphite. (3) Results: The uptake of phenolic compounds from olive leaves through a trunk injection system device resulted in a statistically significant increase in leaf area index and leaf area density, as well as in the growth of newly formed healthy shoots. Plant growth-promoting effects were also observed for potassium phosphite. Moreover, the bacteriostatic activities of the phenolic extract and of the garlic-powder-based solution have been demonstrated in in vitro tests. (4) Conclusions: The results obtained and the contained costs of extraction make the endotherapeutic treatment with phenolic compounds a promising strategy for controlling X fastidiosa to be tested on a larger scale, although the experiments conducted in this study proved not to be suitable for centenary trees.