Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence

Casas, Gianmarco Goycochea; Baselly-Villanueva, Juan Rodrigo; Limeira, Mathaus Messias Coimbra; Torres, Carlos Moreira Miquelino Eleto; Leite, Hélio Garcia

doi:10.1016/j.tfp.2023.100440

Trees, Forests and People

2023

DOI: 10.1016/j.tfp.2023.100440

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Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence

Gianmarco Goycochea Casas,

Juan Rodrigo Baselly-Villanueva,

Mathaus Messias Coimbra Limeira

et al.

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2024

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Cited by 3 publications

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Integrating artificial intelligence in biodiversity conservation: bridging classical and modern approaches

Ullah,

Saqib,

Xiong

2024

Biodivers Conserv

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Integrating artificial intelligence in biodiversity conservation: bridging classical and modern approaches

Ullah,

Saqib,

Xiong

2024

Biodivers Conserv

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Estimates of Soil Organic Carbon in the Ojos de Agua and El Quinillal Forests in the Central Huallaga of Peru

Mendoza-Lopez,

Ordoñez-Sánchez,

Valdez-Andía

et al. 2024

International Journal of Forestry Research

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The Peruvian Amazon has experienced large losses of forest cover due to changes in land use, contributing to increases in CO2 in the atmosphere. This study estimated the organic carbon content of forest soil in two forests “Ojos de Agua” and “El Quinillal” in the Central Huallaga of Peru, establishing three types of cover: (i) primary, (ii) intervened, and (iii) deforested. For this purpose, 24 plots of 100 m2 were established and samples were extracted at a depth of 0–20 cm. The effect of the type of forest cover on soil carbon (Organic Carbon‐SOC, Inorganic Carbon‐SIC, Saturated Carbon‐SC, Critical Carbon‐CC, Saturated Carbon Deficit‐SCD, and Organic Carbon‐OC) was analyzed by means of an Analysis of Variance, correlation. Likewise, the relationship between carbon (C) and soil properties was evaluated by principal component analysis and correlation network. The results indicated that the highest SOC averages were found in the primary forests of Ojos de Agua and El Quinillal with 3.54% and 2.51%. The lowest values were found in the deforested forests with 1.34% and 1.46%. The calculation of the saturated C levels of the soil showed an average of 28.63% ± 2.14% and the saturated carbon deficit of 26.63% ± 2.45%, whereas the critical threshold of C showed an average of 2.21% ± 0.18%. The highest SOC content found in the Ojos de Agua primary forest is due to the presence of dominant forest species such as Manilkara bidentata and Brosimum alicastrum. Likewise, the C deficit in the soils of the study area is very alarming because it is very close to saturation levels, especially in deforested forests.

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Development of an Image Recognition System Based on Neural Networks for the Classification of Plant Species in the Amazon Rainforest, Peru, 2024

Meneses Claudio

2024

LatIA

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Introduction: The recognition and classification of plant species in the Amazon Rainforest is crucial for biodiversity conservation and ecological research. This study presents the development of an image recognition system based on neural networks for the classification of plant species in the Amazon Rainforest, Peru, 2024.Objective: Create an efficient model that can identify and classify various plant species from images, thus improving current methods of cataloging and studying Amazonian flora.Methodology: The methodology includes collecting a large dataset of plant images, followed by rigorous preprocessing to normalize and augment the data. A convolutional neural network (CNN) was designed and trained using advanced machine learning techniques, and its performance was evaluated using metrics such as precision, recall and F1-score.Results: The results show that the developed model achieves an accuracy of 92%, surpassing traditional methods and some previous models in the literature. This high precision suggests that the system can be a valuable tool for researchers and conservationists in the Amazon Rainforest.Conclusion: This study demonstrates the effectiveness of neural networks in the classification of plant species and highlights their potential to contribute significantly to the conservation and study of biodiversity in the Amazon region.

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Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence

Cited by 3 publications

References 48 publications

Integrating artificial intelligence in biodiversity conservation: bridging classical and modern approaches

Integrating artificial intelligence in biodiversity conservation: bridging classical and modern approaches

Estimates of Soil Organic Carbon in the Ojos de Agua and El Quinillal Forests in the Central Huallaga of Peru

Development of an Image Recognition System Based on Neural Networks for the Classification of Plant Species in the Amazon Rainforest, Peru, 2024

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