Development of a responsive optimisation framework for decision-making in precision agriculture

Kong, Qingyuan; Kuriyan, Kamal; Shah, Nilay; Guo, Miao

doi:10.1016/j.compchemeng.2019.106585

Cited by 31 publications

(25 citation statements)

References 38 publications

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“…Además, Srivastava et al (2019), combinan técnicas de Índices de Vegetación Espectral para producir una solución integral para la agricultura de precisión utilizando un UAV y VegNet. También, en (Kong et al, 2019), se desarrolló un marco de optimización receptiva que combina programación lineal de enteros mixtos y algoritmos de búsqueda metaheurísticos híbridos.…”

Section: Resultados Y Discusiónunclassified

Industria 4.0 como herramienta en la agricultura de precisión—monitoreo y control de producción agrícola: Una síntesis de la literatura

Montiel-Rosales¹,

Montalvo-Romero²,

Vásquez³

et al. 2021

Tendencias en La Investigación Universitaria. Una Visión Desde Latinoamérica. Volumen XIV

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Las diversas medidas derivadas por la Declaratoria de Emergencia Sanitaria de aislamiento social y permanencia en casa por el COVID-19, han cambiado el ritmo y forma en la que se desenvuelven los procesos productivos. Es la cadena agroalimentaria, uno de estos sectores productivos que también ha sido afectada y que a pesar de ello no se ha detenido. En México, se considera que el 54% de la producción de alimentos recae en los pequeños productores de baja escala y que son generadores del 80% del empleo. Sí bien es cierto que no se ha detenido dicha cadena de valor también esta actividad productiva está sufriendo los estragos derivados del COVID-19, e.g., reducción de ingresos; aunado a esto, las United Nations considera que para el 2050 se tendrá que alimentar alrededor de 9 mil millones de personas, y que, la dietética de las personas está cambiando. Estos grandes desafíos a los que se enfrenta la producción agrícola rural de baja escala ponen en riesgo la seguridad alimentaria. Ahora bien, por la misma naturaleza del producto —perecedero— se requiere un minucioso seguimiento y control durante todo el proceso del cultivo, que garantice un producto de calidad. El presente artículo analiza e identifica las principales tecnologías de la Industria 4.0, que se emplean en la Agricultura de Precisión, desde una revisión de diferentes documentos de los últimos 5 años en base de datos como Scopus, Google Scholar, páginas web institucionales y repositorios científicos. El estudio realizado arroja que la Agricultura de Precisión, es posible mediante un monitoreo y control de los cultivos, y esto se logra con apoyo de la I4.0. Por lo que, la I4.0, provee herramientas inteligentes y que en tiempo real poseen la capacidad de procesar información compleja, convirtiéndose en un gran aliado del agro-productor a baja escala en el post-COVID-19.

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Section: Resultados Y Discusiónunclassified

Industria 4.0 como herramienta en la agricultura de precisión—monitoreo y control de producción agrícola: Una síntesis de la literatura

Montiel-Rosales¹,

Montalvo-Romero²,

Vásquez³

et al. 2021

Tendencias en La Investigación Universitaria. Una Visión Desde Latinoamérica. Volumen XIV

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“…Agriculture 4.0, thanks to wideband connection technology and cloud storage, allows a real-time view on the farm and creates agro-ecosystems of connected farms and machinery [45], [46]. Agriculture 4.0 brings a new concept of wholefarm management based on the cross-industry cooperation of stakeholders, infrastructures, and technologies [37].…”

Section: A What Is Agriculture 40?mentioning

confidence: 99%

“…It is still not completely clear where the term Agriculture 4.0 comes from. It is explained by many authors as an analogy to the Industry 4.0 revolution [19], [33], [35], [37], [42], [43], [45], [46], [48]- [50]. Indeed, the Industry 4.0 revolution has been characterized by the automation of traditional industrial practices by the integration of advanced digital technologies like the IoT and processes virtualization.…”

Section: B Origins Of Agriculture 40mentioning

confidence: 99%

The Digital Agricultural Revolution: A Bibliometric Analysis Literature Review

et al. 2021

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The application of digital technologies in agriculture can improve traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG) emissions, and promote a sustainable intensification for food security. Some authors argued that we are experiencing a Digital Agricultural Revolution (DAR) that will boost sustainable farming. This study aims to find evidence of the ongoing DAR process and clarify its roots, what it means, and where it is heading. We investigated the scientific literature with bibliometric analysis tools to produce an objective and reproducible literature review. We retrieved 4995 articles by querying the Web of Science database in the timespan 2012-2019, and we analyzed the obtained dataset to answer three specific research questions: i) what is the spectrum of the DAR-related terminology?; ii) what are the key articles and the most influential journals, institutions, and countries?; iii) what are the main research streams and the emerging topics? By grouping the authors' keywords reported on publications, we identified five main research streams: Climate-Smart Agriculture (CSA), Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT), and Artificial Intelligence (AI). To provide a broad overview of each of these topics, we analyzed relevant review articles, and we present here the main achievements and the ongoing challenges. Finally, we showed the trending topics of the last three years (2017, 2018, 2019).INDEX TERMS Agriculture 4.0, bibliometrics, climate-smart agriculture, digital agriculture, literature review, precision agriculture.

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“…These problems can be defined as optimization scenarios in which we can find concrete values associated with weather, irrigation, and nutrients. In this case, the most common approach to solving them is based on meta-heuristics [ 112 , 113 , 114 ].…”

Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

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Development of a responsive optimisation framework for decision-making in precision agriculture

Cited by 31 publications

References 38 publications

Industria 4.0 como herramienta en la agricultura de precisión—monitoreo y control de producción agrícola: Una síntesis de la literatura

Industria 4.0 como herramienta en la agricultura de precisión—monitoreo y control de producción agrícola: Una síntesis de la literatura

The Digital Agricultural Revolution: A Bibliometric Analysis Literature Review

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

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