Coffee pest severity by agrometeorological models in subtropical climate

Aparecido, Lucas Eduardo de Oliveira; Lorençone, Pedro Antônio; Lorençone, João Antônio; Meneses, Kamila Cunha de; Lima, Rafael Fausto de; Moraes, José Reinaldo da Silva Cabral de; Torsoni, Guilherme Botega

doi:10.1007/s00484-022-02252-y

Cited by 2 publications

(1 citation statement)

References 67 publications

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“…Se durante o enchimento de grãos as condições ambientais não forem favoráveis para o acúmulo de fotossíntatos pela planta, provavelmente tem-se um produto de menor qualidade(FAGAN et al, 2011). Utilizando dados do período de 1984 a 2018 para todos os municípios do estado do Paraná coletados da base de dados da NASA POWER,Aparecido et al (2022), verificaram um aumento significativo de ácaro vermelho do sul, broca do café e bicho mineiro em todo o estado do Paraná.Para ambas as áreas de estudo, o aumento da temperatura do ar é a questão mais preocupante (visto que o déficit hídrico pode ser resolvido por irrigação) uma vez que esta variável afeta processos como a fotossíntese, a transpiração, o repouso vegetativo, a duração das fases fenológicas, o florescimento e a germinação(PEREIRA et al, 2002; MAVI e TUPPER, 2004).…”

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Estatística climatológica para áreas produtoras de cafés especiais no Brasil alinhadas a propostas de adaptação às mudanças climáticas

Almussa Leite Torres

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The new global paradigm of coffee marketing and consumption is marked by greater quality control of the grain aligned with environmental preservation and sustainable agricultural management. Brazil, the world's largest producer of grain, has been showing in recent years a greater focus on the production of quality coffees. Permeating these guidelines are the scenarios of climate change that can cause damage to producers, since changes in atmospheric patterns affect the physiological development of the plant reducing the amount of grains produced and the quality of the product. The present study aims to analyze, through statistical tests, the temperature and precipitation variables of the Alta Mogiana Paulista specialty coffee producing region (located in the state of São Paulo) and the coffee producing region with Indication Origin of Northern Pioneer of Paraná. The statistical tests of Mann-Kendall and Pettitt showed trends of increase in temperatures (maximum and minimum) and decrease in precipitation for the two regions. Descriptive statistics were made for the months of flowering of coffee (September and October) and show a downward trend in precipitation and significant increases in temperatures for most of the municipalities studied. From the results, it was verified the need to seek measures to adapt to climate change, already in progress, for the coffee growing of these regions. Thus, a systematic bibliographic review was elaborated without metadata in which it was possible to conclude that agroforestry systems and technology use along the production chain are the main strategies for coping with climate change in coffee growing.

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Estatística climatológica para áreas produtoras de cafés especiais no Brasil alinhadas a propostas de adaptação às mudanças climáticas

Almussa Leite Torres

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New Spectral Index and Machine Learning Models for Detecting Coffee Leaf Miner Infestation Using Sentinel-2 Multispectral Imagery

et al. 2023

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The coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the “Coffee-Leaf-Miner Index” was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR–BLUE and NIR–RED bands was more sensitive for the detection of coffee leaf miner infestation; therefore, the NIR, BLUE, and RED bands were selected to develop the new index. The “Coffee-Leaf-Miner Index” presented the best performance among those evaluated, with a coefficient of determination of about 0.87, a root mean square error of 4.92% coffee leaf miner infestation, accuracy of 89.47%, and kappa coefficient of 95.39. The R2 range of other spectral indices which exist in the literature and which were used in this study was from 0.017 to 0.867, and the root mean square error ranged from 4.996 to 13.582% coffee leaf miner infestation. The machine learning method was then adopted using the supervised Random Forest and Support Vector Machine algorithms to recognize patterns of coffee leaf miner infestation in the field, only the Coffee-Leaf-Miner Index was used for the identification test of the coffee leaf miner infestation. The Support Vector Machine with linear Kernel type was applied to establish a discrimination model. The number of trees for the Random Forest classifier was 100. The Support Vector Machine presented a lower performance than the Random Forest algorithm, but the performance of both were above 80% for user and producer precision. Three bands (Blue, Red, NIR) were selected for the creation of the new index, which showed capacity for remote detection of coffee leaf miner infestation on a regional scale. Thus, “Coffee-Leaf-Miner Index” can identify coffee leaf miner infestation thanks to all the complexity involved in detecting pests via orbital remote sensing.

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Coffee pest severity by agrometeorological models in subtropical climate

Cited by 2 publications

References 67 publications

Estatística climatológica para áreas produtoras de cafés especiais no Brasil alinhadas a propostas de adaptação às mudanças climáticas

Estatística climatológica para áreas produtoras de cafés especiais no Brasil alinhadas a propostas de adaptação às mudanças climáticas

New Spectral Index and Machine Learning Models for Detecting Coffee Leaf Miner Infestation Using Sentinel-2 Multispectral Imagery

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