Optical Sensors Applied in Agricultural Crops

Povh, Fabrício Pinheiro; Anjos, Wagner de Paula Gusmão dos

doi:10.5772/57145

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…To meet such demand, many optical monitoring devices have been developed in recent years [8][9][10][11]. Proximal optical sensors (POSs) base their readings on the detection of chlorophyll, an indicator of N content [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Testing Proximal Optical Sensors on Quinoa Growth and Development

et al. 2020

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Proximal optical sensors (POSs) are effective devices for monitoring the development of crops and the nitrogen (N) status of plants. POSs are both useful and necessary in facilitating the reduction of N losses into the environment and in attaining higher nitrogen use efficiency (NUE). To date, no comparison of these instruments has been made on quinoa. A field experiment conducted in Tuscany, Italy, with different POSs, has assessed the development of quinoa with respect to N status. Three sets of POSs were used (SPAD-502, GreenSeeker, and Canopeo App.) to monitor quinoa development and growth under different types of fertilizers (digestate and urea) and levels of N fertilization (100, 50, and 0 kg N ha−1). The present findings showed that in-season predictions of crop biomass at harvest by SPAD-502 and GreenSeeker optical sensors were successful in terms of the coefficient of determination (R2 = 0.68 and 0.82, respectively) and statistical significance (p < 0.05), while the Canopeo App. was suitable for monitoring the plant´s canopy expansion and senescence. The relative error (RE%) showed a remarkably high performance between observed and predicted values, 5.80% and 4.12% for GreenSeeker and SPAD-502, respectively. Overall, the POSs were effective devices for monitoring quinoa development during the growing season and for predicting dry biomass at harvest. However, abiotic stresses (e.g., heat-stress conditions at flowering) were shown to reduce POSs’ accuracy when estimating seed yields at harvest, and this problem will likely be overcome by advancing the sowing date.

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Section: Introductionmentioning

confidence: 99%

“…Regarding reflectance sensors, ground-based active crop canopy sensors have been developed for site-specific N management in cereals [12,20]. Crop reflectance sensors are also promising methods for monitoring the N status of vegetable crops.…”

Section: Introductionmentioning

confidence: 99%

Testing Proximal Optical Sensors on Quinoa Growth and Development

et al. 2020

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“…Optical sensing systems are potential and suitable tools for assessing mycorrhization success rate since they gather information on plant response, rather than costly, time-consuming, and destructive laboratory analyses. In this field, many efforts are done with various acquisitions sensing system and several spectral features developed (Mulla, 2013;Pinheiro & Gusmo Dos Anjos., 2014) .However, there is an apparent lack of study in remote sensing crop investigation with arbuscular mycorrhizae. New spectral indices that simultaneously allow assessment of multiple crops characteristic and reveal better understanding of plant molecular mechanism could be retrieved.…”

Section: Introductionmentioning

confidence: 99%

Fluorescence and Reflectance Spectroscopy for Early Detection of Different Mycorrhized Plantain Plants

Dibi¹,

Fotso²,

Brou³

et al. 2016

APR

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Sustainable agriculture with use of Arbuscular Mycorrhizal Fungi (AMF) is an emerging farm management that improves crops nutrient and water use efficiency. Decision making on the effect of AMF is still dependent on agronomic diagnosis which is long, tedious, expensive and destructive. This study demonstrates the applicability of proximal fluorescence and reflectance spectroscopy for evaluating and detecting at early stage distinct types of mycorrhized plantain from two cultivars (Musa paradisiaca).Visible-near infrared (400-1000 nm) reflectance and fluorescence data were collected from control and three levels mycorrhized plants designed in randomized and complete block under greenhouse conditions. Two spectral measurements at a week interval were performed on plant leaves by using an USB spectrometer mounted with an Arduino-based LED driver clip.A new normalized reflectance water NWI5 index shows with Datt5 alone highly significant differences at P<0.001 respectively for Orishele and fhia21 cultivars. dNIRmin920_980, NDVI3 and GI reflectance index are significant at P<0.01. Seven other reflectance and 3 fluorescence indices ANTH, FRF_R and NBI_R are significant at P<0.05. The two first principal components for each cultivar spectral features explaining 94.1 % of variance were used to build predictive classification models. LogitBoost algorithm indicates accuracy of 90.27% on stratified cross-validation and 87.5% on test split. Our results confirm that fluorescence and reflectance spectroscopy is a valuable tool for early assessment of mycorrhization success rate evaluation and pattern recognition. They also show promise for the development of non-destructive and cost-effective detectors in monitoring crops under biofertilizers with arbuscular mycorrhizae.

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IoT Based Smart Greenhouse Framework and Control Strategies for Sustainable Agriculture

et al. 2022

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In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. In perspective to the current standing of IoT in agriculture, identification of the most prominent application of IoT-based smart farming i.e. greenhouse has been highlighted and presented a comprehensive systematic literature review (SLR) in the Greenhouse field. The SLR has been conducted by reviewing the articles published between 2015-2022. This SLR analyses 32 relevant studies, which were selected manually from well-reputed digital databases. The primary objective of this SLR is to conduct state-of-the-art research to analyze and identify the current research trends, opportunities, challenges, and gaps in IoT-based greenhouse farming. A rigorous discussion on IoT-based greenhouse applications, sensors/devices, and communication protocols have been presented. Furthermore, this research also presents an inclusive review of IoT-based greenhouse sensors/devices and communication protocols. Moreover, we have also presented a rigorous discussion on smart greenhouse farming challenges and security issues as well as identified future research directions to overcome these challenges. This research has explained many aspects of the technologies involved in IoT-based greenhouse and proposed network architecture, topology, and platforms. In the end, review results have been summarized by presenting an IoT-based greenhouse farm management taxonomy and attacks taxonomy.INDEX TERMS Internet of things (IoT), greenhouse, applications, sensors, communication protocols, cloud computing, big data analytics, security attacks

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Optical Sensors Applied in Agricultural Crops

Cited by 5 publications

References 34 publications

Testing Proximal Optical Sensors on Quinoa Growth and Development

Testing Proximal Optical Sensors on Quinoa Growth and Development

Fluorescence and Reflectance Spectroscopy for Early Detection of Different Mycorrhized Plantain Plants

IoT Based Smart Greenhouse Framework and Control Strategies for Sustainable Agriculture

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