An Automatic Plant Growth Measurement System for Plant Factory

Chen, Wei-Tai; Yeh, Yu-Hui Flora; Liu, Tingyu; Lin, Ta‐Te

doi:10.3182/20130327-3-jp-3017.00073

Cited by 9 publications

(10 citation statements)

References 7 publications

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“…The responses of plant growth as affected by the change of nutrient solution temperature were estimated through the change of the fresh weight of the plant. The fresh weight of the plant provides direct information on the status of the plant, and also an indicator for monitoring plant growth (Oda and Tsuji, 1992;Chen et al, 2013;Helmer, Ehret et al, 2005). In this study, a unique measurement system consisted of a load cell CZL635 micro load cell (Phidgets Inc., Canada), load cell support structure, and plant holder was developed (Figure 1).…”

Section: The Responses Of Plant Growth Measurementmentioning

confidence: 99%

Performance of Long Short-Term Memory Networks for Modeling the Response of Plant Growth to Nutrient Solution Temperature in Hydroponic

2020

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This study examines the development of an approach for modeling the response of plant growth to nutrient solution temperature in hydroponic cultivation in a dynamic system. Nutrient solution temperature is one of the essential manipulating factors for plant growth in hydroponic cultivation. Determining the optimal control strategy of nutrient solution temperature during cultivation could lead to maximize the growth of the plant. By identifying the process using a dynamic system, the optimal control strategy can be determined. However, developing a dynamic model of plant growth to nutrient solution temperature is not easy due to physiological behavior between them are quite complex and uncertain. We propose the long short-term memory (LSTM) networks to identify and develop a model of dynamic characteristics of plant growth as affected by the nutrient solution temperature. Chili pepper plants were used to obtain time-series data of plant growth, with five different types of dynamic nutrient solution temperature patterns for system identification. The results showed that the proposed LSTM model provides promising performance in predicting the response of plant growth to nutrient solution temperature in hydroponic cultivation.

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Section: The Responses Of Plant Growth Measurementmentioning

confidence: 99%

Performance of Long Short-Term Memory Networks for Modeling the Response of Plant Growth to Nutrient Solution Temperature in Hydroponic

2020

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“…In another study, [10] developed a computer stereo-vision system using two cameras with parallel optical axes coupled with an image processing algorithm to monitor Boston lettuce growth automatically in a plant factory. Vision features included projected leaf area, plant height, diameter, and volume.…”

Section: P J M Loresco Is With Electrical and Electronics Engineermentioning

confidence: 99%

Vision-Based Lettuce Growth Stage Decision Support System Using Artificial Neural Networks

Loresco¹,

Dadios²

2020

IJMLC

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Machine vision approaches for lettuce growth stage prediction are continuously being developed. Previous works suggest further extensive study of computer vision features in determining plant growth. This paper presented an ANN-based decision support system of classifying lettuce growth stage by using extracted vision features that included two morphological features (area, perimeter), 12 color features (RGB, HSV, YCbCr, Lab), and five textural features (contrast, energy, correlation, entropy, and homogeneity). Image processing techniques were used to extract the required vision features, and the neural network was trained using scaled conjugate gradient back propagation. The decision support system exhibited promising results in lettuce growth stage classification.

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“…This study is not concerned with the concept, and a fine distinction is not intended. However, from the point of view of their common characteristics, both of them are new agricultural production systems integrating information electronics, automatic control, artificial intelligence, mechanical technology, botany, light quality physiology, horticulture, agronomy, and other disciplines (Chen et al, 2013;Ohara et al, 2015;Khan and Ahmed, 2017;Hu et al, 2018;Liu et al, 2019;Wang et al, 2022a). They have realized the accurate and controllable growth environment of crops, the comprehensive reorganization and optimization of resources and energy consumption, and the overall ecological friendliness of environmental contamination (Ioslovich and Gutman, 2000;Brummer et al, 2011;Beacham et al, 2019;Tuomisto, 2019).…”

Section: Introduction Background Analysismentioning

confidence: 99%

Sustainable production systems of urban agriculture in the future: a case study on the investigation and development countermeasures of the plant factory and vertical farm in China

Wang

Onychko

Зубко

et al. 2023

Front. Sustain. Food Syst.

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IntroductionIn recent years, innovative sustainable agricultural production technologies, including vertical farms and plant factories, have been developing rapidly around the world. The development of plant factories and vertical farms is currently receiving a lot of attention from Chinese academia and industry. However, the recognition and satisfaction of the government, producers, sales companies, and consumers are low, and their attitudes are mixed, mainly due to high pre-construction and post-operation costs, low comprehensive utilization of resources, low product diversity, low market share, high prices, and low core competitiveness, which limit its healthy and sustainable development. This paper designed a questionnaire from the perspectives of industrialization, commercialization, and sustainability in order to understand the respondents' level of awareness, doubts and concerns, purchase intentions and consumption expectations, as well as their trust and recognition of branded products in this new agricultural production system.MethodTo determine the diversity of respondents, this paper examines the group structure of participants from management, research and development, manufacturing, sales, consumers, regional development, and other industrial sectors. The survey was conducted using both face-to-face interviews and electronic questionnaires. As an online survey, the questionnaire was distributed to social groups through social media platforms. A total of 729 valid questionnaires were submitted. For the purposes of categorizing, enumerating, compiling statistics, and analyzing the questionnaires used in this study, descriptive statistics, multi-factor cross-analysis, and other statistical methods were employed. The social roles, functions, and interaction styles of various plant factory practitioners are examined from the perspective of social relationships, and pertinent development concepts and suggestions are proposed based on the survey results.Results and conclusionsThe study found that an increasing number of consumers are understanding and accepting this new form of plant production and are willing to purchase plant products from plant factories and vertical farming. Plant factories and vertical farms are widely regarded as one of the most important methods of future urban agricultural production. Awareness, purchase intent, price expectations, brand awareness, and price expectations of plant factories and vertical farms varied significantly by gender, age, education level, occupation, and income. In addition, there are numerous findings that provide governments, producers, marketers, managers, and consumers with great value and assistance.Development recommendationsWe should take the opportunity of developing plant factories to adjust the structure of the plant industry, enrich the “vegetable basket” of urban residents, increase the supply capacity of the market, enhance agricultural modernization and technological innovation, improve the quality of agricultural products from plant factories, strengthen the brand sales of plant products, and develop more functional plant products with high added value. Through the development of plant factories and vertical farms, we can improve the nutrition and healthy diet structure of citizens' diets, increase the modern plant industry's contribution to the national economy, and promote the comprehensive and sustainable development of the urban productive plant industry.

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An Automatic Plant Growth Measurement System for Plant Factory

Cited by 9 publications

References 7 publications

Performance of Long Short-Term Memory Networks for Modeling the Response of Plant Growth to Nutrient Solution Temperature in Hydroponic

Performance of Long Short-Term Memory Networks for Modeling the Response of Plant Growth to Nutrient Solution Temperature in Hydroponic

Vision-Based Lettuce Growth Stage Decision Support System Using Artificial Neural Networks

Sustainable production systems of urban agriculture in the future: a case study on the investigation and development countermeasures of the plant factory and vertical farm in China

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