• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.518-525
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• 2012

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

• Journal of Biosystems Engineering
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• v.24 no.1
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• pp.25-30
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• 1999

The effectiveness of many greenhouse environment control methodologies depends on the growth information of crops. Acquisition of the growth information of crops requires a non-invasive and continuous monitoring method. Crop growth monitoring system using digital imaging technique was developed to conduct non-destructive and intact plant growth analyses. The monitoring system automatically measures crop growth information sends an appropriate control signal to the nutrient solution supplying system. To develop the monitoring system, a linear model that explains the relationship between the fresh weight and the top projected leaf area of a lettuce plant was developed from an experiment. The monitoring system was evaluated buy successive lettuce growing experiments. Results of the experiments showed that the developed system could estimate the fresh weight of lettuce from a lettuce image by using the linear model and generate an EC control signal according to the lettuce growth stage.

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.439-444
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• 1999

Automated greenhouse production systems often require crop growth monitoring involving accurate quantification of plant physiological properties. Conventional methods are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system can accomplish rapid and accurate measurements of physiological-property changes of stressed crops. In this research a thermal imaging system was used to measure the leaf-temperature changes of several crops according to water deficit. Thermal images were obtained from lettuce, cucumber, pepper, and chinese cabbage plants. Results showed that there were significant differences in the temperature of stressed plants and non-stressed plants. The temperature differences between these two group of plants were 0.7 to 3$^{\circ}C$ according to species.

• Journal of Biosystems Engineering
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• v.25 no.4
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• pp.293-300
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• 2000

Automated greenhouse production system often require crop growth monitoring involving accurate quantification of plant physiological properties. Conventional methods are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system can accomplish rapid and accurate measurements of physiological-property changes of stressed crops. In this research a thermal imaging system was used to measure the leaf-temperature changes of several crops according to nutrient stresses. Thermal images were obtained from lettuce, cucumber, and pepper plants. Plants were placed in growth chamber to provide relatively constant growth environment. Results showed that there were significant differences in the temperature of stressed plants and non-stressed plants. In a case of the both N deficiency and excess, the leaf temperatures of cucumber were $2^{\circ}C$ lower than controlled temperature. The leaf temperature of cucumber was $2^{\circ}C$ lower than controlled temperature only when it was under N excess stress. For the potassium deficiency or excess stress, the leaf temperaures of cucumber and hot pepper were $2^{\circ}C$ lower than controls, respectively. The phosphorous deficiency stress dropped the leaf temperatures of cucumber and hot pepper $2^{\circ}C$ and $1.5^{\circ}C$ below than controls. However, the leaf temperature of lettuce did not change. It was possible to detect the changes in leaf temperature by infrared thermography when subjected to nutrition stress. Since the changes in leaf temperatures were different each other for plants and kinds of stresses, however, it is necessary to add a nutrient measurement system to a plant-growth monitoring system using thermography.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.