Quantification of flowering in coffee growing with low-cost RGB sensor UAV-mounted

Cano, Mario de Jesús Agudelo; Marulanda, Edison Eduardo Callejas; Céspedes, Vladimir Henao; Cardona-Morales, Oscar; Garcés-Gómez, Yeison Alberto

doi:10.1016/j.scienta.2022.111649

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…AI-based recognition systems assist early detection and classification of crop diseases, pests, and weeds, using remote sensing and multispectral cameras (Sun et al, 2018). Remote monitoring and optimization of irrigation and fertilization systems using IoT sensors, AI-based data analysis, and decision support systems are emerging soil management trends in Agriculture 4.0 (Agudelo Cano et al, 2023). Intelligent water quality monitoring systems with IoT sensors are deployed to measure various parameters for effective water management.…”

Section: Introductionmentioning

confidence: 99%

“…Colombia has recognized the potential of IoT to improve productivity and sustainability in the agricultural sector. Initiatives have been launched to adopt IoT sensors in fruit orchards, enabling continuous monitoring of environmental conditions and facilitating precision farming practices (Pineda, Pérez et al, 2022;Pineda, Tinoco et al, 2022;Agudelo Cano et al, 2023). These sensors provide farmers with valuable insights and enable early detection of issues such as diseases or pests for prompt and targeted interventions (Ramírez Alberto et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

Pardo-Pardo,

Cuervo-Bejarano

2023

Agron. Colomb.

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Agriculture 4.0 refers to innovations in technological tools used in agriculture to achieve different objectives, such as adapting the supply chain to avoid waste, increasing productivity and collecting mass data through ICT (Information and Communication Technologies) to meet the growing food demand of the population. The objective of this study is to conduct a systematic literature review to evaluate the impact of emerging technologies on sustainable fruit production. Initially, a bibliographic search was conducted on the technologies currently implemented in agriculture; the Bibliometrix library of the R Studio software was used, and then an analysis of relevant scientific publications published in the last ten years was carried out through the VOSviewer® software, which allowed the construction and visualization of bibliometric networks. The results show Europe and China as the leading regions in technological development, while developing countries face economic and research limitations; in Colombia, the use of Agriculture 4.0 is focused on the implementation of satellite images for monitoring agro-climatic conditions. In summary, Agriculture 4.0 aims to achieve economic, social, and environmental sustainability in the agri-food sector through data-generating technologies to improve production, reduce costs, and ensure food safety and quality. However, there is a technology gap between developed and developing countries that affects the adoption of these innovations. More support is therefore needed from governments, academia, and the private sector to drive innovation, training, and adoption of these technologies, which can contribute to the economic, social, and environmental development of the country.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

Pardo-Pardo,

Cuervo-Bejarano

2023

Agron. Colomb.

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A Micro-Scale Approach for Cropland Suitability Assessment of Permanent Crops Using Machine Learning and a Low-Cost UAV

et al. 2023

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This study presents a micro-scale approach for the cropland suitability assessment of permanent crops based on a low-cost unmanned aerial vehicle (UAV) equipped with a commercially available RGB sensor. The study area was divided into two subsets, with subsets A and B containing tangerine plantations planted during years 2000 and 2008, respectively. The fieldwork was performed on 27 September 2021 by using a Mavic 2 Pro UAV equipped with a commercial RGB sensor. The cropland suitability was performed in a two-step classification process, utilizing: (1) supervised classification with machine learning algorithms for creating a vegetation mask; and (2) unsupervised classification for the suitability assessment according to the Food and Agriculture Organization of the United Nations (FAO) land suitability standard. The overall accuracy and kappa coefficients were used for the accuracy assessment. The most accurate combination of the input data and parameters was the classification using ANN with all nine input rasters, managing to utilize complimentary information regarding the study area spectral and topographic properties. The resulting suitability levels indicated positive suitability in both study subsets, with 63.1% suitable area in subset A and 59.0% in subset B. Despite that, the efficiency of agricultural production can be improved by managing crop and soil properties in the currently non-suitable class (N1), providing recommendations for farmers for further agronomic inspection. Alongside low-cost UAV, the open-source GIS software and globally accepted FAO standard are expected to further improve the availability of its application for permanent crop plantation management.

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Quantification of flowering in coffee growing with low-cost RGB sensor UAV-mounted

Cited by 2 publications

References 14 publications

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

A Micro-Scale Approach for Cropland Suitability Assessment of Permanent Crops Using Machine Learning and a Low-Cost UAV

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