Lettuce growth prediction in plant factory using image processing technology

Kim, Myeong H.; Choi, Eun G.; Baek, Gyeong Yun; Kim, Chi H.; Jink, Byeong O.; Moon, Byeong Eun; Kim, Dong E.; Kim, Hyeon T.

doi:10.3182/20130327-3-jp-3017.00036

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…Early applications of color imaging to monitor lettuce growth were aimed to investigate the possible use of sensed plant-projected area extracted from top-view images to identify nutrient stress in hydroponic cultivation ( Giacomelli et al, 1998 ). More recently, similar approaches have been applied to lettuce growth-rate data extracted from greenhouse imaging, to be used as state-variable to feedback control of nutrient solution in hydroponic system ( Jung et al, 2015 ) or for other crop management operations ( Kim et al, 2013 ). Bumgarner et al (2012) , by conducting an extensive study on imaging of lettuce plants grown in different environments, concluded that a top-view approach is an accurate method to indirectly measure lettuce biomass during the early stages of growth, while on canopy closure the correlation is weakened by occlusions in plant’s top-view due to leaves overlapping.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Borage Extracts As Potential Biostimulant Using a Phenomic, Agronomic, Physiological, and Biochemical Approach

et al. 2017

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Biostimulants are substances able to improve water and nutrient use efficiency and counteract stress factors by enhancing primary and secondary metabolism. Premise of the work was to exploit raw extracts from leaves (LE) or flowers (FE) of Borago officinalis L., to enhance yield and quality of Lactuca sativa ‘Longifolia,’ and to set up a protocol to assess their effects. To this aim, an integrated study on agronomic, physiological and biochemical aspects, including also a phenomic approach, has been adopted. Extracts were diluted to 1 or 10 mL L–1, sprayed onto lettuce plants at the middle of the growing cycle and 1 day before harvest. Control plants were treated with water. Non-destructive analyses were conducted to assess the effect of extracts on biomass with an innovative imaging technique, and on leaf photosynthetic efficiency (chlorophyll a fluorescence and leaf gas exchanges). At harvest, the levels of ethylene, photosynthetic pigments, nitrate, and primary (sucrose and total sugars) and secondary (total phenols and flavonoids) metabolites, including the activity and levels of phenylalanine ammonia lyase (PAL) were assessed. Moreover, a preliminary study of the effects during postharvest was performed. Borage extracts enhanced the primary metabolism by increasing leaf pigments and photosynthetic activity. Plant fresh weight increased upon treatments with 10 mL L–1 doses, as correctly estimated by multi-view angles images. Chlorophyll a fluorescence data showed that FEs were able to increase the number of active reaction centers per cross section; a similar trend was observed for the performance index. Ethylene was three-fold lower in FEs treatments. Nitrate and sugar levels did not change in response to the different treatments. Total flavonoids and phenols, as well as the total protein levels, the in vitro PAL specific activity, and the levels of PAL-like polypeptides were increased by all borage extracts, with particular regard to FEs. FEs also proved efficient in preventing degradation and inducing an increase in photosynthetic pigments during storage. In conclusion, borage extracts, with particular regard to the flower ones, appear to indeed exert biostimulant effects on lettuce; future work will be required to further investigate on their efficacy in different conditions and/or species.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Borage Extracts As Potential Biostimulant Using a Phenomic, Agronomic, Physiological, and Biochemical Approach

et al. 2017

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“…However, their analyses were conducted after the wheat had finished growing. Chen et al [ 12 ] and Kim et al [ 13 ], who proposed image processing methods for evaluating plant growth, were similarly constrained because the evaluation was once again conducted after plant growth had been completed. In these methods, the evaluation takes at least several months to wait for the plant growth to finish and reach maturity.…”

Section: Introductionmentioning

confidence: 99%

A simple adaptive difference algorithm with CO2 measurements for evaluating plant growth under environmental fluctuations

et al. 2022

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Objective The aim of this study is to demonstrate an adaptive method that is robust toward environmental fluctuations and provides a real-time measure of plant growth by measuring CO2 consumption. To verify the validity of the proposed method, the relation between the plant growth and variation in light conditions with a closed experimental system was investigated. Results The proposed method was used to measure the photosynthetic rate induced by photosynthetic photon flux density (PPFD) and to evaluate plant growth under continuous and pulsed light in arugula plants. The PPFD-dependent change in photosynthetic rate was measured. And in the condition range of 200–10,000 μs pulse period and 50% duty ratio of pulsed light, there was no change in the growth rate of plants assuming the same PPFD as continuous light. These experiments showed the validity of the adaptive method in removing environmental fluctuations without precise control of temperature and humidity.

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“…The growth stage of chili plants is typically measured manually, as discussed by Sharma & Kumar (2017). The manual way of identifying growth stages is inconsistent because the human factor and the process are time-consuming (Kim et al, 2013). Nevertheless, the manual process still needs to be performed due to the requirement of different amounts of fertilizer and water for each growth stage, which is necessary for optimizing crop yields (Wayne, 2021).…”

Section: Introductionmentioning

confidence: 99%

Efficacy of Deep Learning Algorithm in Classifying Chilli Plant Growth Stages

Rozilan

Hanafi²,

Ali³

et al. 2021

Adv Agric Food Res J

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Automatic plant growth monitoring has received considerable attention in recent years. The demand in this field has created various opportunities, especially for automatic classification using deep learning methods. In this paper, the efficiency of deep learning algorithms in classifying the growth stage of chili plants is studied. Chili is one of the high cash value crops, and automatic identification of chili plant growth stages is essential for crop productivity. Nevertheless, the study on automatic chili plant growth stage classification using deep learning approaches is not widely explored, and this is due to the unavailability of public datasets on the chili plant growth stages. Various deep learning methods, namely Inception V3, ResNet50, and VGG16, were used in the study, and the results have shown that these methods performed well in terms of accuracy and stability when tested on a dataset that consists of 2,320 images of Capsicum annum 'Bird's Eye' plants of various growth stages and imaging conditions. Nevertheless, the results have also shown that the deep learning methods have difficulty classifying images with a complex background where more than one chili plant was captured in an image.

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Lettuce growth prediction in plant factory using image processing technology

Cited by 8 publications

References 3 publications

Evaluation of Borage Extracts As Potential Biostimulant Using a Phenomic, Agronomic, Physiological, and Biochemical Approach

Evaluation of Borage Extracts As Potential Biostimulant Using a Phenomic, Agronomic, Physiological, and Biochemical Approach

A simple adaptive difference algorithm with CO2 measurements for evaluating plant growth under environmental fluctuations

Efficacy of Deep Learning Algorithm in Classifying Chilli Plant Growth Stages

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