Visual Classification of Lettuce Growth Stage based on Morphological Attributes using Unsupervised Machine Learning models

Alejandrino, Jonnel; Concepcion, Ronnie; Lauguico, Sandy; Tobias, Rogelio Ruzcko; Almero, Vincent Jan D.; Puno, John Carlo V.; Bandala, Argel A.; Dadios, Elmer P.; Flores, Ramón

doi:10.1109/tencon50793.2020.9293854

Cited by 36 publications

(4 citation statements)

References 19 publications

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“…In soil-based agriculture, lettuce root length may descend up to 28 inches down the ground and 12 inches horizontal span for root branches. As nutrients are mobilized into the plant photosynthetic system, root traits particularly during the early embryonic life cycle resemble crop yield (Alejandrino et al, 2020;Moreira, Martins, & Mourato, 2020;Wu, Asaduzzaman, Shephard, Hopwood, & Ma, 2020) Evidently, seed architecture has a direct relation with seed yield (Strock et al, 2019). Potent nitrogen and salt concentration (Fu 2013), and copper oxide nanoparticles (Margenot et al, 2018;Wang & Shen, 2012;Wang et al, 2020) in grow bed must seriously be managed to avoid speeding up the degradation of roots.…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Concepcion

Dadios

2021

Agrivita.J.Agr.Sci

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Ecophysiological stimulators directly affect root morphology, especially in the embryonic stage. To enhance crop germination, an understanding of the root traits under abiotic inducers is needed. In this study, the combined impacts of white and red-blue light spectrums, cold stratification, and seed fortification involving various concentrations of bioactive chemicals namely simple nutrient addition program solution, gibberellic acid, α-naphthaleneacetic acid with thiamine hydrochloride were evaluated on loose-leaf lettuce (Lactuca sativa var. Altima) seedling root architecture. The growth-promoting effects of these nutrients varied the growth rate and morphology of roots which are immediately shown during the radicle development. Integrated computer vision and computational intelligence were employed for phytomorphological signatures extraction of seedlings that were cultivated in a customized modulable spectrum experimental chamber (MSPEC). Root phenotype model was developed using graph-cut segmentation and region properties, and the ideal germination nutrient concentration was optimized using bioinspired models with firefly algorithm optimal result of 204.1 mg/L for nitrate, 238.15 mg/L for phosphate, and 158.08 mg/L for potassium. It was verified that lettuce seedlings can endure highly concentrated nutrients, however, it is more sensitive to phosphate as this macronutrient significantly promotes root growth with the increased whorl number on white light spectrum exposure with cold stratification.

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Section: Introductionmentioning

confidence: 99%

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Concepcion

Dadios

2021

Agrivita.J.Agr.Sci

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“…Here, the plant leaf was used as an essential feature [21]. Techniques like Self-Organizing Maps (SOM), hierarchical clustering, and k-means algorithm were utilized for lettuce crop growth prediction with the extracted feature plant leaf and achieved higher accuracy rates [22]. Data visualization and Logistic regression approaches were used for analyzing the distribution of the dataset of the lettuce crop and produced the average error rates while predicting the lettuce yield [23].…”

Section: Survey Of Literaturementioning

confidence: 99%

Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models

Rajendiran,

Rethnaraj

2024

RIA

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“…A two‐layer NN supports decision‐making of irrigating by inputting plant growth, temperature, humidity and soil moisture. K‐means is an unsupervised ML method which had the greatest precision in Alejandrino et al (2020). Segmented features were extracted from plant images to identify the development growth.…”

Section: Ai Applications In Soil Management and Agricultural Productionmentioning

confidence: 99%

AI‐enhanced soil management and smart farming

Chen

Chong³

et al. 2021

Soil Use and Management

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Soil and crop management are facing challenges, in particular an increasing pressure to feed a growing population and improve food safety. Smart farming assists in dealing with these issues by incorporating information and communication technologies into farming practice. The Multiponics Vertical Farming (MVF) system is a very promising concept in smart farming that saves space, transportation cost and delivery time. Complicated systems and messy data can be processed by advanced technologies to achieve greater accuracy and efficiency in modelling and computing. For dynamic and complex problems, Artificial Intelligence (AI) provides rapid evaluation and aids in designing effective solutions. This study discusses how AI is adopted in soil management and MVF for tasks including classification, detection and forecasting. Three categories of AI and machine learning techniques, Support Vector Machine (SVM), Decision Tree (DT) and Neural Network (NN)‐based models, that are widely used will be presented. Future perspectives on urban farming are also discussed.

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Visual Classification of Lettuce Growth Stage based on Morphological Attributes using Unsupervised Machine Learning models

Cited by 36 publications

References 19 publications

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models

AI‐enhanced soil management and smart farming

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