Comparação entre amostragem foliar convencional e de precisão para análise de micronutrientes na cafeicultura

Carvalho, Luis Carlos Cirilo; Silva, Fábio Moreira da; Ferraz, Gabriel Araújo e Silva; Figueiredo, Vanessa; Cunha, João Paulo Barreto

doi:10.25186/cs.v12i2.1278

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The non-uniform grid sampling of two points per ha totaled 112 points and was recommended for precision coffee growing, besides pointed in the studies of optimal grid index (Carvalho et al, 2017;Ferraz et al, 2017b;Figueiredo et al, 2017). The commercial software of precision agriculture called FARMWORKS was used for pointing and creating the grid sampling and sample points, later transferred in a text file with geographic coordinate data and identification of points.…”

Section: Methodsmentioning

confidence: 99%

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Technical and economic viability of manual harvesting coffee yield maps

Faria

Silva

Ferraz

et al. 2020

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Precision coffee growing is a concept that implies the use of precision agriculture techniques in coffee plantations. For the coffee growing, the precision electronic resources coupled to the harvesters are very scarce. Thereby, the harvest of coffee plantations that compose the grid sampling for generation of thematic maps can be performed manually. The aim of the present study was to generate a linear regression model to estimate the time required to harvest, estimate the labor costs to harvest manually the georeferenced sample points for generation of coffee yield maps. The study was performed in a coffee area of 56 hectares using two sampling points per hectare, totaling 112 points, being evaluated four coffee plants for each point. The manual harvest of the points was performed by four rural workers with experience in the coffee harvest. Afterwards, the collected volume was measured by a graduated container and the times were obtained by the digital stopwatch. Based on the data obtained in the field, a linear correlation model was established between the harvest time of each sampling point and the yield of the point, whose R² value was 78.27, cost was R$ 8.92 per point. These results are relevant for estimating the amount of labor force required to generate manually harvest yield maps according to the producer's coffee yield estimate, contributing to the closure of the precision coffee growing cycle.

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Section: Methodsmentioning

confidence: 99%

“…For perennial crops, predominantly the coffee crop, the precision management techniques are still limited. The conventionally used way to sample coffee crop properties is made using random sampling, as prescribed by classical, nonspatial statistics (Carvalho et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Technical and economic viability of manual harvesting coffee yield maps

Faria

Silva

Ferraz

et al. 2020

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“…Embora a agricultura de precisão seja uma ótima opção para o manejo mais adequado e correto dos atributos físicoquímicos do solo e da planta, boa parcela dos cafeicultores persiste no sistema de manejo convencional e são resistentes à implementação da cafeicultura de precisão, seja pela falta de informação técnica confiável que comprove os benefícios desta técnica, ou por questões culturais onde o produtor insiste em manejos tradicionalmente aplicados em seu meio familiar. Por essa razão é necessário a concretização de pesquisas que supram as deficiências de informações comparando os dois sistemas produtivos (Carvalho et al, 2017).…”

Section: Introductionunclassified

Análise espacial dos atributos químicos do solo e geração de mapas de aplicação de fertilizantes e corretivos na Cultura do Café

Silva

Marques

Ciacci

et al. 2022

RSD

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A agricultura de precisão é uma das opções disponíveis para redução de custo no cultivo do café, melhorando a eficiência das adubações, aumentando a produtividade. Neste sentido, objetivou-se neste trabalho avaliar a distribuição espacial dos atributos químicos do solo e a geração de mapas de aplicação de fertilizantes e corretivos do solo a taxas variadas na cultura do café, além de estimar a produção da cultura e a correlação entre a fertilidade e a produção. O experimento foi conduzido na Fazenda Bela Vista, no município de Três Corações, Sul de Minas Gerais em uma área de 2,4 ha de Café Arábica, variedade Mundo Novo 376/4, com 4 anos de idade, plantada no espaçamento de 3,5 x 1 m, totalizando de 2.857 plantas/ha. Para tanto, gerou-se um grid amostral de 35 m x 35 m totalizando 24 pontos amostrais, onde foram coletadas 24 amostras de solo compostas georreferenciadas, sob a projeção da copa do cafeeiro, para análise da fertilidade. Identificou-se e descreveu-se a organização da dependência espacial do pH, M.O, Ca, Mg, V%, P, K e CTC e, as recomendações de Super Fosfato Simples, Cloreto de Potássio, Ureia e Calcário, que foram ajustadas por meio de semivariograma. O emprego da Krigagem possibilitou a elaboração dos mapas, permitindo observar a variabilidade espacial e realizar o manejo de forma localizada e precisa. Pôde-se perceber que a diferença dos atributos químicos nas recomendações convencional e da agricultura de precisão, da mesma forma é compreensível as diferenças a serem aplicadas em cada uma das técnicas.

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Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning

de Oliveira Faria,

Filho,

Santana

et al. 2024

J Sci Food Agric

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BackgroundCoffee farming constitutes a substantial economic resource. This crop represents a source of income for several countries due to the high consumption of coffee drinks worldwide. Precise management of coffee crops involves collecting crop attributes (soil, plant), mapping, and applying inputs according to the plant's needs. This differentiated management is Precision Coffee Growing stands out for its increased yield and sustainability.ResultsThus, this research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data was obtained in a field of 54.6ha during two consecutive seasons, applying varied fertilization rates according to recommendations of soil attributes maps. Furthermore, monitoring leaf analysis maps seeks to establish a correlation between input parameters and yield prediction. The machine learning models obtained from this data efficiently predicted coffee yield. The best model demonstrated predictive fit results of 0.86 Pearson correlation. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models.ConclusionThese findings have important implications for optimizing coffee management and cultivation, providing valuable insights for producers and researchers interested in maximizing yield using precision agriculture.This article is protected by copyright. All rights reserved.

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Comparação entre amostragem foliar convencional e de precisão para análise de micronutrientes na cafeicultura

Cited by 4 publications

References 7 publications

Technical and economic viability of manual harvesting coffee yield maps

Technical and economic viability of manual harvesting coffee yield maps

Análise espacial dos atributos químicos do solo e geração de mapas de aplicação de fertilizantes e corretivos na Cultura do Café

Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning

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