As environmental pollution and the global population increase, and the COVID-19 pandemic becomes more severe, demands for indoor farming, especially home food gardening, have also increased. However, most research thus far has focused on large-scale food production, with very few studies having been conducted at the household scale. Also, the devices cultivating household crops with control systems in a continuous way, which minimize fluctuations of environmental conditions, have been rarely developed. Therefore, this study aimed to design a household cultivation system for sweet basil that is automatically and continuously controlled by fuzzy logic with a Raspberry Pi4. Three inputs (temperature, humidity, and growth stage) and seven outputs (fan, humidifier, heater 1, heater 2, LED red, green, and blue) were used with six rules, ensuring that three lights operated independently upon three growth stages. Simulation and actual operation were carried out, resulting in an appropriately controlled system that operated with few defects. In the case of an operation of the input variable, temperature and humidity were maintained at an average of 21.24 °C and 75.58%, respectively, and the LED operation for the growth stage was confirmed to be flawless. For verification of the designed fuzzy system, a comparison between the simulation and actual operation was performed to examine differences and identify problems. To this end, Pearson’s correlation coefficients were used, and the direction of correction of the fuzzy logic system was proposed. Through these results, the feasibility of a home cultivation system using fuzzy logic was demonstrated, and it is expected that further studies applying it will be conducted in the future.
Beyond the use of 2D images, the analysis of 3D images is also necessary for analyzing the phenomics of crop plants. In this study, we configured a system and implemented an algorithm for the 3D image reconstruction of red pepper plant (Capsicum annuum L.), as well as its automatic analysis. A Kinect v2 with a depth sensor and a high-resolution RGB camera were used to obtain more accurate reconstructed 3D images. The reconstructed 3D images were compared with conventional reconstructed images, and the data of the reconstructed images were analyzed with respect to their directly measured features and accuracy, such as leaf number, width, and plant height. Several algorithms for image extraction and segmentation were applied for automatic analysis. The results showed that the proposed method showed an error of about 5 mm or less when reconstructing and analyzing 3D images, and was suitable for phenotypic analysis. The images and analysis algorithms obtained by the 3D reconstruction method are expected to be applied to various image processing studies.
The prediction and early detection of physiological disorders based on the nutritional conditions and stress of plants are extremely vital for the growth and production of crops. High-throughput phenotyping is an effective nondestructive method to understand this, and numerous studies are being conducted with the development of convergence technology. This study analyzes physiological disorders in plant leaves using hyperspectral images and deep learning algorithms. Data on seven classes for various physiological disorders, including normal, prediction, and the appearance of symptom, were obtained for strawberries subjected to artificial treatment. The acquired hyperspectral images were used as input for a convolutional neural network algorithm without spectroscopic preprocessing. To determine the optimal model, several hyperparameter tuning and optimizer selection processes were performed. The Adam optimizer exhibited the best performance with an F1 score of ≥0.95. Moreover, the RMSProp optimizer exhibited slightly similar performance, confirming the potential for performance improvement. Thus, the novel possibility of utilizing hyperspectral images and deep learning algorithms for nondestructive and accurate analysis of the physiological disorders of plants was shown.
Far-red light was excluded in photosynthetic photon flux; however, recent studies have shown that it increases photosynthetic capacity. In addition, there were few studies on the whole canopy photosynthetic rate and continuous changes of morphology on cucumber seedlings affected by far-red light. This study evaluated the effect of conventional white LEDs adding far-red light on cucumber seedlings using a semi-open chamber system for the measurement of the whole canopy gas exchange rate, and the Raspberry Pi-based imaging system for the analysis of a continuous image. In the image, through the imaging system, it was confirmed that far-red light promoted the germination rate of cucumber seedlings and enhanced early growth. However, the dry weight of the shoot and root did not increase. The measured net apparent CO2 assimilation rate was improved by an increasing leaf area during the cultivation period. The conventional white LED light source with added far-red light increased the photosynthetic rate of cucumber seedlings’ whole canopy. However, at the early seedling stage, plant height and leaf area of the whole canopy was increased by far-red light, and it was revealed that the image data saturated faster. It was considered that the photosynthetic efficiency decreased due to a shading effect of the limited planting density of the cell tray. The results found that using appropriate far-red light, considering planting density, could increase the photosynthetic rate of the whole canopy of crops, thereby promoting crop growth, but it was judged that the use of far-red light in the early growth stage of cucumber seedlings should be considered carefully.
This study was conducted to investigate the weight loss, firmness, external color and vitamin C (VC) content of tomatoes (Lycopersicon esculentum) using non-destructive method to measure identical tomato samples during 15 days storage at low temperature and high humidity. Tomatoes were harvested at the light red stage, sorted, box packed and then stored in thermo-hygrostat (10±1℃, 90±10%RH). The quality changes in weight loss, firmness and external color were measured every 3 day interval. Weight loss was increased by 1.13±0.15%, but it may not be considered to affect quality. Surface color of fruit was changed, especially in lightness and hue angle value. The color values were analyzed by analysis of variance (ANOVA), and the results were significant (p<0.001). Firmness of fruit declined during storage, but it did not decrease in direct proportion. On the storage of day 15, firmness was decreased to 40% of initial state. At last, all the experiment data are summarized and the relationship between firmness and weight loss is analyzed to construct a linear regression mathematical model that can predict the weight loss with the firmness value measured by non-destructive method. This research result could be useful in helping tomato exporters and suppliers to get real-time quality factor by using proposed method and regression model.
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