Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms are increasing in the development of agricultural robots since deep learning algorithms that use convolutional neural networks have been proven to show outstanding performance in image classification, segmentation, and object detection. However, most of these applications are focused on the development of harvesting robots, and thus, there are only a few studies that improve and develop monitoring robots through the use of deep learning. For this reason, we aimed to develop a real-time robot monitoring system for the generative growth of tomatoes. The presented method detects tomato fruits grown in hydroponic greenhouses using the Faster R-CNN (region-based convolutional neural network). In addition, we sought to select a color model that was robust to external light, and we used hue values to develop an image-based maturity standard for tomato fruits; furthermore, the developed maturity standard was verified through comparison with expert classification. Finally, the number of tomatoes was counted using a centroid-based tracking algorithm. We trained the detection model using an open dataset and tested the whole system in real-time in a hydroponic greenhouse. A total of 53 tomato fruits were used to verify the developed system, and the developed system achieved 88.6% detection accuracy when completely obscured fruits not captured by the camera were included. When excluding obscured fruits, the system’s accuracy was 90.2%. For the maturity classification, we conducted qualitative evaluations with the assistance of experts.

In this study, we aimed to develop a prediction model of the solid solutions concentration (SSC) and moisture content (MC) in oriental melon with snapshot-type hyperspectral imagery (Visible (VIS): 460–600 nm, 16 bands; Red-Near infrared (Red-NIR): 600–860 nm, 15 bands) using a machine learning model. The oriental melons were cultivated in a hydroponic greenhouse, Republic of Korea, and a total of 91 oriental melons that were harvested from March to April of 2022 were used as samples. The SSC and MC of the oriental melons were measured using destructive methods after taking hyperspectral imagery of the oriental melons. The reflectance spectrum obtained from the hyperspectral imagery was processed by the standard normal variate (SNV) method. Variable importance in projection (VIP) scores were used to select the bands related to SSC and MC. As a result, ten (609, 736, 561, 849, 818, 489, 754, 526, 683, and 597 nm) and six (609, 736, 561, 818, 849, and 489 nm) bands were selected for the SSC and MC, respectively. Four machine learning models, support vector regression (SVR), ridge regression (RR), K-nearest neighbors regression (K-NNR), and random forest regression (RFR), were used to develop models to predict SSC and MC, and their performances were compared. The SVR showed the best performance for predicting both the SSC and MC of the oriental melons. The SVR model achieved a relatively high accuracy with R² values of 0.86 and 0.74 and RMSE values of 1.06 and 1.05 for SSC and MC, respectively. However, it will be necessary to carry out more experiments under various conditions, such as differing maturities of fruits and varying light sources and environments, to achieve more comprehensive predictions and apply them to monitoring robots in the future. Nevertheless, it is considered that the snapshot-type hyperspectral imagery aided by SVR would be a useful tool to predict the SSC and MC of oriental melon. In addition, if the maturity classification model for the oriental melon can be applied to fields, it could lead to less labor and result in high-quality oriental melon production.

It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.

In this paper, the potential of a Maillard reaction-based time–temperature indicator (TTI) as a device for the visualization of shelf life was evaluated by comparing the quality variations of cold-stored strawberries and the color changes of Maillard reaction solutions. The color variations of the Maillard reaction solutions stored in the same storage environment as the cold-stored strawberries showed suitable characteristics for the visualization of shelf life, such as pronounced color changes, a wide-ranging color variation rate, and activation energy. In particular, the concentrations of the reaction solutions with the combinations of 3.0 M D-xylose, 1.5 M glycine, and 0.3 M Na₂HPO₄; 2.0 M D-xylose, 1.5 M glycine, and 0.3 M Na₂HPO₄; and 4.0 M D-xylose, 1.0 M glycine, and 0.3 M Na₂HPO₄ were suitable for the visualization of the weight loss variation, color difference variation, and appearance damage. These concentrations showed similar color variations and Arrhenius activation energies to the variation of the quality characteristics, such as weight loss and color difference. Additionally, a Maillard reaction-based time–temperature indicator (TTI) has a wide applicability to other agricultural products using the wide range of the color change rate and the activation energy.

(1) Background: We studied how the generation position of air anions in a closed-type plant factory affects the growth characteristics of lettuce and identified the optimal position. (2) Methods: We used LEDs (red/green/blue = 8:1:1) as a light source and set the temperature and RH of the plant factory to 20 ± 2 ℃ and 50% ± 5%, respectively. We grew lettuce under three air anion conditions—sideward, upward, and downward—and compared the growth characteristics to those of a control grown without air anions. We measured the growth characteristics of the lettuce at 3 and 4 weeks after sowing, and the measurement items were shoot fresh weight (FW) and dry weight (DW); leaf area (LA), length (LL), and width (LW); SPAD; antioxidant capacity; and total phenol content. (3) Results: At 4 weeks, FW in the downward treatment condition was 25.3% higher than in the control, and DW showed a similar difference. LA was about 1943.94 cm²/plant in the downward treatment condition, which was about 15.5% higher than in the control. (4) Conclusions: We conclude that air anion generation has a positive effect on lettuce growth, and the optimal generation position for air anions is downward.

In Korea, tomatoes are primarily grown in greenhouses, which are widely distributed through the country. Greenhouse cultivation provides greater stability and control of the environment, including light, temperature, and carbon dioxide levels, resulting in higher quality and yield compared to open fields. Therefore, greenhouse environment management is a important because quality and yield, which are directly related to profits, could vary depending on greenhouse environment. In addition, developing growth model for crops grown hydroponic system in the greenhouse is useful for describing and comparing the response of crops in terms of environmental conditions. In this study, we use the logistic growth equation to describe the growth of tomatoes. The logistic growth equation is used to model the color change of tomatoes, and we compared the differences in the growth process of tomatoes at two different growing seasons by using the parameters in the model. According to the logistic model, tomatoes grew slightly faster in winter than in summer and the discoloration period of tomatoes, in terms of changing from green to red, was about two days shorter in winter than in summer. In the process of growing, there was no significant difference in the daily average temperature and accumulated temperature in summer and winter. However, the solar radiation inside the greenhouse was 590.7 J/cm 2 in winter and 347.4 J/cm 2 in summer, which was higher in winter than in summer. This is because that the screen was used to maintain temperature and reduce the light intensity on very sunny day in summer. This study shows unexpected results that the growing period of tomatoes is shorter in winter than in summer due to the way of greenhouse environmental management. Therefore, based on these results, it is necessary that we need to consider the appropriate ways of controlling environments in greenhouse, depending on growing season.