Institute for innovation, capacity building and sustainability of agri-food production

Funder

Organizational Unit

Publications

Remote Sensing Applications in Almond Orchards: A Comprehensive Systematic Review of Current Insights, Research Gaps, and Future Prospects

Publication . Guimarães, Nathalie; Sousa, Joaquim J.; Pádua, Luís; Bento, Albino; Couto, Pedro

Almond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security. The present systematic literature review, conducted according to the PRISMA protocol, is devoted to the applications of remote sensing technologies in almond orchards, a relatively new field of research. The study includes 82 articles published between 2010 and 2023 and provides insights into the predominant remote sensing applications, geographical distribution, and platforms and sensors used. The analysis shows that water management has a pivotal focus regarding the remote sensing application of almond crops, with 34 studies dedicated to this subject. This is followed by image classification, which was covered in 14 studies. Other applications studied include tree segmentation and parameter extraction, health monitoring and disease detection, and other types of applications. Geographically, the United States of America (USA), Australia and Spain, the top 3 world almond producers, are also the countries with the most contributions, spanning all the applications covered in the review. Other studies come from Portugal, Iran, Ecuador, Israel, Turkey, Romania, Greece, and Egypt. The USA and Spain lead water management studies, accounting for 23% and 13% of the total, respectively. As far as remote sensing platforms are concerned, satellites are the most widespread, accounting for 46% of the studies analyzed. Unmanned aerial vehicles follow as the second most used platform with 32% of studies, while manned aerial vehicle platforms are the least common with 22%. This up-to-date snapshot of remote sensing applications in almond orchards provides valuable insights for researchers and practitioners, identifying knowledge gaps that may guide future studies and contribute to the sustainability and optimization of almond crop management.

2024Journal article

Open access

Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction

Publication . Guimarães, Nathalie; Fraga, Helder; Sousa, Joaquim J.; Pádua, Luís; Bento, Albino; Couto, Pedro

Almonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Tras-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.

2024Journal article

Open access

Identification of aphids using machine learning classifiers on UAV-based multispectral data

Publication . Guimarães, Nathalie; Pádua, Luís; Sousa, Joaquim J.; Bento, Albino; Couto, Pedro

Almond trees in Portugal are susceptible to aphid infestation, which can result in reduced fruit production. To effectively tackle this issue, the combination of remote sensing (RS) data and machine learning (ML) classifiers can be used to accurately detect the presence of aphids. This study focuses in the implementation of ML classifiers and RS data analysis to identify aphids on almond trees, using high-resolution multispectral data collected through an unmanned aerial vehicle (UAV) in a Portuguese almond orchard. Four ML classifiers, kNN, SVM, RF and XGBoost, were employed and fine-tuned using vegetation indices derived from spectral data. The results revealed that the SVM classifier achieved an overall accuracy (OA) of 77%, followed by kNN with an OA of 74%, while XGBoost and RF achieved OAs of 71% and 69%, respectively. Consequently, this study demonstrates the viability of employing RS data and ML classifiers for aphid identification in almond orchards.

2023Conference object

Open access

The role of natural compounds in rat mammary cancer: the beneficial effects of Santolina chamaecyparissus L. aqueous extract

Publication . Azevedo, Tiago; Silva, Jessica; Peixoto, Francisco P.; Silvestre-Ferreira, Ana C.; Gama, Adelina; Seixas, Fernanda; Finimundy, Tiane C.; Barros, Lillian; Matos, Manuela; Oliveira, Paula A.; Faustino-Rocha, Ana; Valada, Abigaël; Anjos, Lara; Moura, Tânia; Ferreira, Rafaela; Santos, Marlene; Pires, Maria João; Neuparth, Maria João

Breast cancer is the most diagnosed cancer among women, and a leading cause of death worldwide. Santolina chamaecyparissus L. is a plant with multiple health benefits, including anticancer and anti-diabetic properties. This study aimed to assess the chemopreventive effects of S. chamaecyparissus aqueous extract (SCE) in an animal model of mammary cancer. A total of 28 four-week-old female Wistar rats were divided into four groups: control, MNU-induced (IND), SCE-supplemented (SCE), and SCE+IND. SCE was added to drinking water (12.72 mg/kg body weight) ad libitum. MNU was administered via the intraperitoneal route at 50 days of age. Weekly monitoring of body weight, food/drink intake, humane endpoints, and number of mammary tumours were recorded. Twenty weeks after MNU administration, animals were sacrificed by anaesthetic overdose and a necropsy was performed. Blood samples were used to determine blood count and serum biochemistry analysis, while kidney and liver samples were analysed for oxidative stress. Tumour samples were collected for gene expression and histology studies. SCE chemical composition was analysed by LC-MS and contained 19 phenolic compounds, with the most abundant being myricetin-O-glucuronide and 1,3-O-dicaffeoylquinic acid. Two animals in the IND group were sacrificed due to exceeding the humane endpoint limits. SCE supplementation delayed mammary tumour development, reducing its volume and weight. SCE had a positive impact on haematological parameters, particularly the neutrophil-lymphocyte ratio (P=0.026). No significant differences were observed in serum biochemistry, except for creatinine kinase MB, or in oxidative stress markers. Gene expression analysis showed significantly reduced VEGF expression levels (P=0.0158) in tumours from SCE+IND. These findings suggest that SCE is deserving of further study to identify the individual compounds and to understand its influence on animal models during cancer development.

2024Journal article

Open access

From Waste to Resource: Compositional Analysis of Olive Cake’s Fatty Acids, Nutrients and Antinutrients

Publication . Paié-Ribeiro, Jessica; Baptista, Filipa; Teixeira, José; Guedes, Cristina; Gomes, Maria J.; Teixeira, Alfredo; Barros, Ana Novo; Pinheiro, Victor; Outor-Monteiro, Divanildo

The olive oil industry, recognised for its beneficial products for health and food culture, generates a significant amount of by-products that, if not appropriately managed, can pose considerable environmental challenges. This study examined six olive cakes (OC) from the Trás-os-Montes and Alto Douro regions, collected on different dates and mills: two obtained by pressing (COC), two by centrifugation (TPOC), including one partially pitted and one dehydrated, and two exhausted (EOC), which were subjected to conventional chemical analyses, namely dry matter (DM), organic matter (OM), crude fat (CF), crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL) profiling fatty acid (FA) and phosphorus and phytic acid content. The dehydrated TPOC had only 8% moisture content (due to drying), followed by EOC with 10% and COC (65–79%). The CF content was high in COC 1 (14.5% in DM), residual in EOC (1.5%) and intermediate in TPOC (9–10%). CP ranged from 5.3 to 7.3%. Notably, NDF levels were high (>65% in 5 samples; pitted TPOC 57.4%) and very lignified (ADL > 23%). Different FA profiles were observed: COC had the highest monounsaturated (76.36 g/100 g), while EOC had the highest saturated (16.56 g/100 g) and polyunsaturated (14.14 g/100 g). Phosphorus and phytic acid content (g/100 g) of EOC 2, TPOC pitted, TPOC dehydrated, COC 1 and COC 2 showed similar values to each other (mean of 0.12 ± 0.02 and 0.44 ± 0.0, respectively), with EOC 1 having the lowest levels (0.07 ± 0.01 and 0.26 ± 0.04, respectively). These results highlight the potential of OCs, especially dry TPOC, which offers transport, conservation and utilisation benefits.

2024Journal article

Open access