1
|
López-Tobar R, Herrera-Feijoo RJ, Mateo RG, García-Robredo F, Torres B. Botanical Collection Patterns and Conservation Categories of the Most Traded Timber Species from the Ecuadorian Amazon: The Role of Protected Areas. PLANTS (BASEL, SWITZERLAND) 2023; 12:3327. [PMID: 37765489 PMCID: PMC10536464 DOI: 10.3390/plants12183327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/05/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
The Ecuadorian Amazon is home to a rich biodiversity of woody plant species. Nonetheless, their conservation remains difficult, as some areas remain poorly explored and lack georeferenced records. Therefore, the current study aims predominantly to analyze the collection patterns of timber species in the Amazon lowlands of Ecuador and to evaluate the conservation coverage of these species in protected areas. Furthermore, we try to determine the conservation category of the species according to the criteria of the IUCN Red List. We identified that one third of the timber species in the study area was concentrated in three provinces due to historical botanical expeditions. However, a worrying 22.0% of the species had less than five records of presence, and 29.9% had less than ten records, indicating a possible underestimation of their presence. In addition, almost half of the species evaluated were unprotected, exposing them to deforestation risks and threats. To improve knowledge and conservation of forest biodiversity in the Ecuadorian Amazon, it is recommended to perform new botanical samplings in little-explored areas and digitize data in national herbaria. It is critical to implement automated assessments of the conservation status of species with insufficient data. In addition, it is suggested to use species distribution models to identify optimal areas for forest restoration initiatives. Effective communication of results and collaboration between scientists, governments, and local communities are key to the protection and sustainable management of forest biodiversity in the Amazon region.
Collapse
Affiliation(s)
- Rolando López-Tobar
- Facultad de Ciencias Agrarias y Forestales, Universidad Técnica Estatal de Quevedo (UTEQ), Quevedo Av. Quito km, 1 1/2 Vía a Santo Domingo de los Tsáchilas, Quevedo 120550, Ecuador;
- Escuela Técnica Superior de Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Unidad de Posgrado, Universidad Técnica Estatal de Quevedo (UTEQ), Quevedo Av. Quito km, 1 1/2 Vía a Santo Domingo de los Tsáchilas, Quevedo 120550, Ecuador
| | - Robinson J. Herrera-Feijoo
- Facultad de Ciencias Agrarias y Forestales, Universidad Técnica Estatal de Quevedo (UTEQ), Quevedo Av. Quito km, 1 1/2 Vía a Santo Domingo de los Tsáchilas, Quevedo 120550, Ecuador;
- Unidad de Posgrado, Universidad Técnica Estatal de Quevedo (UTEQ), Quevedo Av. Quito km, 1 1/2 Vía a Santo Domingo de los Tsáchilas, Quevedo 120550, Ecuador
- Escuela de Doctorado, Centro de Estudios de Posgrado, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente, nº 2, 28049 Madrid, Spain
- Departamento de Biología (Botánica), Facultad de Ciencias, Universidad Autónoma de Madrid, 28049 Madrid, Spain;
| | - Rubén G. Mateo
- Departamento de Biología (Botánica), Facultad de Ciencias, Universidad Autónoma de Madrid, 28049 Madrid, Spain;
- Centro de Investigación en Biodiversidad y Cambio Global (CIBC-UAM), Facultad de Ciencias, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Fernando García-Robredo
- Departamento de Ingeniería y Gestión Forestal y Ambiental, Escuela Técnica Superior de Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, C/José Antonio Novais 10, 28040 Madrid, Spain;
| | - Bolier Torres
- Facultad de Ciencia de la Vida, Universidad Estatal Amazónica (UEA), Puyo 160101, Ecuador;
- Ochroma Consulting and Services, Puerto Napo, Tena 150150, Ecuador
| |
Collapse
|
2
|
Hussein BR, Malik OA, Ong WH, Slik JWF. Applications of computer vision and machine learning techniques for digitized herbarium specimens: A systematic literature review. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
3
|
Dai G, Fan J. An Industrial-Grade Solution for Crop Disease Image Detection Tasks. FRONTIERS IN PLANT SCIENCE 2022; 13:921057. [PMID: 35832228 PMCID: PMC9272756 DOI: 10.3389/fpls.2022.921057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/24/2022] [Indexed: 05/03/2023]
Abstract
Crop leaf diseases can reflect the current health status of the crop, and the rapid and automatic detection of field diseases has become one of the difficulties in the process of industrialization of agriculture. In the widespread application of various machine learning techniques, recognition time consumption and accuracy remain the main challenges in moving agriculture toward industrialization. This article proposes a novel network architecture called YOLO V5-CAcT to identify crop diseases. The fast and efficient lightweight YOLO V5 is chosen as the base network. Repeated Augmentation, FocalLoss, and SmoothBCE strategies improve the model robustness and combat the positive and negative sample ratio imbalance problem. Early Stopping is used to improve the convergence of the model. We use two technical routes of model pruning, knowledge distillation and memory activation parameter compression ActNN for model training and identification under different hardware conditions. Finally, we use simplified operators with INT8 quantization for further optimization and deployment in the deep learning inference platform NCNN to form an industrial-grade solution. In addition, some samples from the Plant Village and AI Challenger datasets were applied to build our dataset. The average recognition accuracy of 94.24% was achieved in images of 59 crop disease categories for 10 crop species, with an average inference time of 1.563 ms per sample and model size of only 2 MB, reducing the model size by 88% and the inference time by 72% compared with the original model, with significant performance advantages. Therefore, this study can provide a solid theoretical basis for solving the common problems in current agricultural disease image detection. At the same time, the advantages in terms of accuracy and computational cost can meet the needs of agricultural industrialization.
Collapse
Affiliation(s)
- Guowei Dai
- National Agriculture Science Data Center, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- *Correspondence: Guowei Dai
| | - Jingchao Fan
- National Agriculture Science Data Center, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China
- Jingchao Fan
| |
Collapse
|