Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars (Lactuca sativa L.)

Deep learning (DL) and computer vision applications in precision agriculture have great potential to identify and classify plant and vegetation species. This study presents the applicability of DL modeling with computer vision techniques to analyze the nutrient levels of hydroponically grown four lettuce cultivars (Lactuca sativa L.), namely Black Seed, Flandria, Rex, and Tacitus. Four different nutrient concentrations (0, 50, 200, 300 ppm nitrogen solutions) were prepared and utilized to grow these lettuce cultivars in the greenhouse. RGB images of lettuce leaves were captured. The results showed that the developed DL’s visual geometry group 16 (VGG16) and VGG19 architectures identified the nutrient levels of lettuces with 87.5 to 100% accuracy for four lettuce cultivars, respectively. Convolution neural network models were also implemented to identify the nutrient levels of the studied lettuces for comparison purposes. The developed modeling techniques can be applied not only to collect real-time nutrient data from other lettuce type cultivars grown in greenhouses but also in fields. Moreover, these modeling approaches can be applied for remote sensing purposes to various lettuce crops. To the best knowledge of the authors, this is a novel study applying the DL technique to determine the nutrient concentrations in lettuce cultivars.

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EC Sensitivity of Hydroponically-Grown Lettuce (Lactuca sativa L.) Types in Terms of Nitrate Accumulation

Agriculture ◽

10.3390/agriculture11040315 ◽

2021 ◽

Vol 11 (4) ◽

pp. 315

Author(s):

Noémi Kappel ◽

Ildikó Fruzsina Boros ◽

Francia Seconde Ravelombola ◽

László Sipos

Keyword(s):

Lactuca Sativa ◽

Nitrate Content ◽

Saline Condition ◽

Fresh Weight ◽

Nitrate Level ◽

Nitrate Accumulation ◽

Strong Negative Correlation ◽

Lactuca Sativa L ◽

Hydroponically Grown ◽

European Markets

The goal of this research was to investigate the effect of electrical conductivity (EC) levels of the nutrient solution on the fresh weight, chlorophyll, and nitrate content of hydroponic-system-grown lettuce. The selected cultivars are the most representative commercial varieties grown for European markets. Seven cultivars (‘Sintia,’ ‘Limeira,’ ‘Corentine,’ ‘Cencibel,’ ‘Kiber,’ ‘Attiraï,’ and ‘Rouxaï’) of three Lactuca sativa L. types’ (butterhead, loose leaf, and oak leaf) were grown in a phytotron in rockwool, meanwhile the EC level of the nutrient solutions were different: normal (<1.3 dS/m) and high (10 dS/m). The plants in the saline condition had a lower yield but elevated chlorophyll content and nitrate level, although the ‘Limeira’ and ‘Cencibel’ cultivars had reduced nitrate levels. The results and the special characteristic of the lollo-type cultivars showed that the nitrate level could be very different due to salinity (‘Limeira’ had the lowest (684 µg/g fresh weight (FW)) and ‘Cencibel’ had the highest (4396 µg/g FW)). There was a moderately strong negative correlation (−0.542) in the reverse ratio among the chlorophyll and nitrate contents in plants treated with a normal EC value, while this relationship was not shown in the saline condition. Under the saline condition, cultivars acted differently, and all examined cultivars stayed under the permitted total nitrate level (5000 µg/g FW).

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Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

Concurrent Engineering ◽

10.1177/1063293x211003194 ◽

2021 ◽

pp. 1063293X2110031

Author(s):

Maolin Yang ◽

Auwal H Abubakar ◽

Pingyu Jiang

Keyword(s):

Deep Learning ◽

Complex Network ◽

Network Models ◽

Neural Network Models ◽

Complex Network Theory ◽

New Type ◽

Manufacturing Resource ◽

Manufacturing Resources ◽

Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

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Intraspecific Variability Largely Affects the Leaf Metabolomics Response to Isosmotic Macrocation Variations in Two Divergent Lettuce (Lactuca sativa L.) Varieties

Plants ◽

10.3390/plants10010091 ◽

2021 ◽

Vol 10 (1) ◽

pp. 91

Author(s):

Giandomenico Corrado ◽

Luigi Lucini ◽

Begoña Miras-Moreno ◽

Leilei Zhang ◽

Christophe El-Nakhel ◽

...

Keyword(s):

Lactuca Sativa ◽

Plant Secondary Metabolites ◽

Intraspecific Variability ◽

Mineral Elements ◽

Yield And Quality ◽

Lactuca Sativa L ◽

Adaptive Mechanisms ◽

Health Related ◽

Multiple Mineral ◽

Hydroponically Grown

Mineral elements are essential for plant growth and development and strongly affect crop yield and quality. To cope with an everchanging environment, plants have developed specific responses to combined nutrient variations. In this work, we investigated the effects of multifactorial treatments with three macrocations (K, Ca, and Mg) on lettuce (Lactuca sativa L.) varieties that strongly diverge in leaf pigmentation (full red or green). Specifically, we monitored main leaf parameters and metabolomics profiles of hydroponically grown plants fed with isosmotic nutrient solutions that have different proportions of macroelements. The result revealed a high biochemical plasticity of lettuce, significantly affected by the genotype, the nutrient solution, and their interaction. Our work also provided evidence and insights into the different intraspecific responses to multifactorial variation of macrocations, with two varieties having distinct strategies to metabolically respond to nutrient variation. Overall, plant adaptive mechanisms increased the phytochemical diversity between the varieties both among and within the main classes of plant secondary metabolites. Finally, our work also implies that the interaction of a pre-existing phytochemical diversity with the management of multiple mineral elements can offer added health-related benefits to the edible product specific to the variety.

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Feature extraction-based image steganalysis using deep learning

WEENTECH Proceedings in Energy ◽

10.32438/wpe.182021 ◽

2021 ◽

pp. 188-198

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Secure Communication ◽

Information Technologies ◽

Network Models ◽

Error Rates ◽

Multimedia Data ◽

Secret Message ◽

Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

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RESPON PERTUMBUHAN TANAMAN SELADA (Lactuca Sativa L) TERHADAP VOLUME AIR DAN KONSENTRASI LARUTAN NUTRISI GOOD-PLANT SECARA HIDROPONIK

Jurnal Agroristek ◽

10.47647/jar.v2i2.182 ◽

2019 ◽

Vol 2 (2) ◽

pp. 43-51

Author(s):

Nuryulsen Safridar, Sri Handayani

Keyword(s):

Plant Height ◽

Lactuca Sativa ◽

Leaf Age ◽

Treatment Plant ◽

Leaf Length ◽

Plant Nutrients ◽

Leaf Number ◽

Water Volume ◽

Nutrient Concentrations ◽

Lactuca Sativa L

This study aims to determine the volume of water and the concentration of the nutrient solution and the right good plant to plant growth of lettuce (lactuca sativa L). This research has been carried out in the garden experiment Jabal Ghafur Faculty of Agriculture, University of Sigli. Runs from February to April 2017. This study used a raft floating hydroponics system. Research using completely randomized design (CRD) factorial pattern that is factor of the volume of water and nutrient concentration factor of good-plant. Treatment of the water volume (V) consists of three levels ie (V1) 4 liters of water, (V2) 8 liters of water and (V3) 12 liters of water. Good treatment-plant nutrient concentrations (N) consists of three levels ie (N1) 600 ppm (N2) of 800 ppm and (N3) 1000 ppm, with three replications so overall deplore 27 experimental unit. The volume of water very significant effect on plant height and leaf length aged 10, 20 and 30 days after planting, leaf number aged 20 and 30 days after planting, heavy wet stover age 30 HST, significantly affect the amount of leaf age 10 HST. Good-plant nutrients very significant effect on plant height ages of 20 and 30 days after planting, leaf number and length of leaf age 30 HST, significant effect on plant height HST age 10, age 20 HST leaf length, weight of wet age 30 HST stover. Keywords: lettuce, hydroponics, water volume and concentration of good-plant nutrients

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Unified Framework for Vision Inference on the Edge

10.36227/techrxiv.11971383 ◽

2020 ◽

Author(s):

Vysakh S Mohan

Keyword(s):

Computer Vision ◽

Mobile Robotics ◽

Low Cost ◽

Network Models ◽

Ease Of Use ◽

Use Cases ◽

Unified Framework ◽

Neural Network Models ◽

Inference Models ◽

Small Form Factor

Edge processing for computer vision systems enable incorporating visual intelligence to mobile robotics platforms. Demand for low power, low cost and small form factor devices are on the rise.This work proposes a unified platform to generate deep learning models compatible on edge devices from Intel, NVIDIA and XaLogic. The platform enables users to create custom data annotations,train neural networks and generate edge compatible inference models. As a testimony to the tools ease of use and flexibility, we explore two use cases — vision powered prosthetic hand and drone vision. Neural network models for these use cases will be built using the proposed pipeline and will be open-sourced. Online and offline versions of the tool and corresponding inference modules for edge devices will also be made public for users to create custom computer vision use cases.

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Deep Network Guided Proof Search

10.29007/8mwc ◽

2018 ◽

Cited By ~ 1

Author(s):

Sarah Loos ◽

Geoffrey Irving ◽

Christian Szegedy ◽

Cezary Kaliszyk

Keyword(s):

Deep Learning ◽

Program Analysis ◽

Network Models ◽

Theorem Prover ◽

Neural Network Models ◽

Two Phase ◽

First Order ◽

Proof Search ◽

System Verification ◽

Theory Reasoning

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go.Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification.Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved.Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

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SEALing Neural Network Models in Encrypted Deep Learning Accelerators

10.1109/dac18074.2021.9586199 ◽

2021 ◽

Author(s):

Pengfei Zuo ◽

Yu Hua ◽

Ling Liang ◽

Xinfeng Xie ◽

Xing Hu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Models ◽

Neural Network Models

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Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

Machine Learning and Knowledge Extraction ◽

10.3390/make1010027 ◽

2019 ◽

Vol 1 (1) ◽

pp. 450-465 ◽

Cited By ~ 8

Author(s):

Abhishek Sehgal ◽

Nasser Kehtarnavaz

Keyword(s):

Deep Learning ◽

Real Time ◽

Open Source Software ◽

Mobile Applications ◽

Network Models ◽

Software Tools ◽

Learning Tools ◽

Smartphone Apps ◽

Learning Models ◽

Neural Network Models

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.

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