Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation

Sulistyo, Susanto Budi; Wu, Di; Woo, Wai Lok; Dlay, S.S.; Gao, Bin

doi:10.1109/tase.2017.2770170

Cited by 40 publications

(30 citation statements)

References 39 publications

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“…ML provides various analytical models and methods to analyze the crop disease, yield prediction and so on. The authors Susanto B et al [38] used the vision sensing approach to estimate nutrient contents in the wheat leaves. Nutrient estimation is necessary to avoid over fertilizing to the crops, which in turn harms the crop as well as environment.…”

Section: Related Workmentioning

confidence: 99%

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Singh

Aernouts

Meyer

et al. 2020

Sensors

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The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design.

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Section: Related Workmentioning

confidence: 99%

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Singh

Aernouts

Meyer

et al. 2020

Sensors

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“…e artificial neural network (ANN), inspired by the structure and the way the human brain works, is extensively applied for classification and prediction in many fields. Particularly, with self-learning, adaptive and self-organizing characteristics, the BP-ANN model is very suitable for recognition of patterns in complex systems and has a strong ability to deal with nonlinear problems [23][24][25][26]. Considering the correlation between the input variables, principal component analysis (PCA) was used for dimension reduction before BP-ANN modelling [27].…”

Section: Data Modellingmentioning

confidence: 99%

Development and Validation of a Photoplethysmography System for Noninvasive Monitoring of Hemoglobin Concentration

Liu

Peng

et al. 2020

Journal of Electrical and Computer Engineering

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Background. Traditional invasive hemoglobin (Hb) detection led to delayed diagnosis, operational inefficiency, incorrect critical decision making, and uncomfortable patient experience. To facilitate real-time total hemoglobin (tHb) monitoring, a portable prototype of a noninvasive Hb detection system was developed, and the accuracy of Hb predicted based on partial least squares (PLS) as well as backpropagation artificial neural network (BP-ANN) models was validated. Results. The prototype was combined with a signal processing circuit and a spectrophotometric probe containing 8 wavelength LEDs light source and photodiode array. Laboratory invasive Hb (Lab_tHb) and spot check Hb measurements with PLS (SpHb_PLS) and BP (SpHb_BP) methods were obtained simultaneously by hematology analyzer and the designed system. The invasive and noninvasive estimates of the Hb levels were analyzed using Spearman correlation as well as Bland–Altman plot and receiver operating characteristic (ROC) curve analysis. A total of 238 volunteers had attempted laboratory invasive and noninvasive spot check Hb measurements. Mean Lab_tHb, SpHb_PLS, and SpHb_BP were 13.6 ± 1.80 g/dL, 13.5 ± 1.07 g/dL, and 13.6 ± 1.06 g/dL, respectively. Noninvasive SpHb_PLS (r = 0.61, p<0.001) and SpHb_BP (r = 0.62, p<0.001) had a strong correlation with invasive tHb values. The Bland–Altman plot showed excellent consistency between the proposed noninvasive methods and laboratory invasive reference. In ROC analysis, PLS and BP models were good at predicting Hb ≥ 12 g/dL with area under the curve of 0.828 and 0.824, respectively. Observed differences between invasive and noninvasive Hb measurements displayed no significant correlation with perfusion index values. Conclusions. The result confirmed that noninvasive Hb monitoring had an excellent correlation with traditional invasive Hb measurement. Furthermore, it is suggested that the developed prototype has the potential for the noninvasive detection of Hb concentration with the methods of PLS and BP-ANN.

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“…For this reason, Refs. [23][24][25] presented a method for predicting nitrogen content in wheat plant. Their methods were based on a segmentation algorithm that was trained in three light intensities for separating wheat plants from the background.…”

Section: Introductionmentioning

confidence: 99%

Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

et al. 2020

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The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.

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Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation

Cited by 40 publications

References 39 publications

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Development and Validation of a Photoplethysmography System for Noninvasive Monitoring of Hemoglobin Concentration

Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

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