A Review of Human-Computer Interaction Design Approaches towards Information Systems Development

Nowadays modern information systems (emerging technologies) are increasingly becoming an integral part of our daily lives and has begun to pose a serious challenge for human-computer interaction (HCI) professionals, as emerging technologies in the area of mobile and cloud computing, and internet of things (IoT), are calling for more devotion from HCI experts in terms of systems interface design. As the number of mobile platforms users, nowadays comprises of children’s, elderly people, and people with disabilities or disorders, all demanding for an effective user interface that can meet their diverse needs, even on the move, at anytime and anywhere. This paper, review current articles (43) related to HCI interface design approaches to modern information systems design with the aim of identifying and determining the effectiveness of these methods. The study found that the current HCI design approaches were based on desktop paradigm which falls short of providing location-based services to mobile platforms users. The study also discovered that almost all the current interface design standard used by HCI experts for the design of user’s interface were not effective &amp; supportive of emerging technologies due to the flexibility nature of these technologies. Based on the review findings, the study suggested the combination of Human-centred design with agile methodologies for interface design, and call on future works to use qualitative or quantitative approach to further investigate HCI methods of interface design with much emphasis on cloud-based technologies and other organizational information systems.</em

Producción agrícola espacial-temporal del Citrus x limon y Mangifera indica, mediante firmas espectrales e imágenes de satélite

Agricultural production of Citrus x limon (lemon) and Mangifera indica (mango) in the Piura region is often affected by environmental-climatic factors, mainly by possible seasonal changes or extreme weather events, such as droughts or El Niño. The objective is to analyze the spatial-temporal agricultural production of lemon and mango, measuring with the FieldSpec4 spectroradiometer, the spectral signature (SF) and Sentinel 2 satellite images (ISS2) of the Chulucanas criollo mango in crops of Pampa Larga-Alvarados-Suyo-Ayabaca and INIA-Hualtaco-Tambogrande-Piura mango germplasm bank, respectively. The method consists of entering each EF in the ISS2 (2019) mosaic of tiles 17MMR-17MNR-17MPR-17MMQ-17MNQ-17MPQ-17MMP-17MMP-17MNP-17MPP, using SEN2COR280 in SNAP software. The time series of monthly/annual production of lemon and mango were analyzed using data from INEI and SIEA-MIDAGRI-PERU. The results obtained estimate a lemon cultivated area of 27 451.84 ha and mango cultivated area of 22000 ha; higher than the reported harvested area of 16113 ha and 20606 ha, respectively. Mango production 1970-2020, is higher in November-December-January-February, explained by the harvested area in 84.1%, showing seasonality, exponential growth behavior and positive (2003-2020) and negative (1970-2002) anomalies. Monthly lemon production 2007-2020 is seasonal, the annual trend increases by 2.8% despite the existence of negative anomalies in 2017, generated by the effects of the "Coastal El Niño" in its evolutionary flowering process, forecasting improvement in lemon production in Piura, between 2021 and 2022.La producción agrícola del Citrus x limon (limón) y Mangifera indica (mango) en la región Piura, muchas veces se ve afectada por factores ambientales-climáticos, principalmente por posibles cambios estacionales o eventos climáticos extremos, como las sequías o El Niño. El objetivo es analizar la producción agrícola espacial-temporal del limón y mango, midiendo con el espectroradiómetro FieldSpec4, la firma espectral (FE) e imágenes de satélite Sentinel 2 (ISS2) del mango criollo de Chulucanas en cultivos de Pampa Larga-Alvarados-Suyo-Ayabaca y banco de germoplasma de mango INIA-Hualtaco-Tambogrande-Piura, respectivamente. El método consiste en introducir cada FE en el mosaico de ISS2 (2019), de los tiles 17MMR-17MNR-17MPR-17MMQ-17MNQ-17MPQ-17MMP-17MNP-17MPP, utilizando SEN2COR280 en el software SNAP. Se analizó las series de tiempo de la producción mensual/anual del limón y del mango, con datos del INEI y SIEA-MIDAGRI-PERÚ. Los resultados obtenidos estiman un área cultivada de limón de 27451,84 ha y de mango de 22000 ha; mayores a la superficie cosechada de 16113 ha y 20606 ha reportadas, respectivamente. La producción de mango 1970-2020, es mayor en noviembre-diciembre-enero-febrero, explicada por el área de superficie cosechada en un 84,1%, presentando estacionalidad, comportamiento exponencial de crecimiento y anomalías positivas (2003-2020) y negativas (1970-2002). La producción mensual de limón 2007-2020 es estacional, la tendencia anual incrementa en 2,8% a pesar de existir anomalías negativas el 2017, generada por efectos del “Niño Costero” en su proceso evolutivo de floración; pronosticando mejoría en la producción de limón en Piura, entre el 2021 al 2022

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions