Agroindustrial valorization of the pulp and peel, seed, flour, and oil of moriche (Mauritia flexuosa) from the Bita River, Colombia: a potential source of essential fatty acids

Edible Fruits from the Ecuadorian Amazon: Ethnobotany, Physicochemical Characteristics, and Bioactive Components

In the Ecuadorian Amazon region, there are various types of edible fruits that have distinct qualities and benefits. Understanding the uses, properties, and functions of these fruits is important for researching products that are only available in local markets. This review aims to gather and summarize the existing scientific literature on the ethnobotany, physicochemical composition, and bioactive compounds of these native fruits to highlight the potential of the region's underutilized biodiversity. A systematic review was carried out following the PRISMA methodology, utilizing databases such as Web of Science, Scopus, Pubmed, Redalyc, and SciELO up to August 2023. The research identified 55 edible fruits from the Ecuadorian Amazon and reported their ethnobotanical information. The most common uses were fresh fruit consumption, preparation of typical food, and medicine. Additionally, nine native edible fruits were described for their physicochemical characteristics and bioactive components: Aphandra natalia (Balslev and Henderson) Barfod; Eugenia stipitate McVaugh; Gustavia macarenensis Philipson; Mauritia flexuosa L.f; Myrciaria dubia (Kunth) McVaugh; Oenocarpus bataua Mart; Plukenetia volubilis L.; Pouteria caimito (Ruiz and Pav.) Radlk.; and Solanum quitoense Lam. The analyzed Amazonian fruits contained bioactive compounds such as total polyphenols, flavonoids, carotenoids, and anthocyanins. This information highlights their potential as functional foods and the need for further research on underutilized crops.

Aerial Identification of Amazonian Palms in High-Density Forest Using Deep Learning

This paper presents an integrated aerial system for the identification of Amazonian Moriche palm (Mauritia flexuosa) in dense forests, by analyzing the UAV-captured RGB imagery using a Mask R-CNN deep learning approach. The model was trained with 478 labeled palms, using the transfer learning technique based on the well-known MS COCO framework©. Comprehensive in-field experiments were conducted in dense forests, yielding a precision identification of 98%. The proposed model is fully automatic and suitable for the identification and inventory of this species above 60 m, under complex climate and soil conditions.