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.

Plant phenotyping has been widely studied as an effective and powerful tool for analyzing crop status and growth. However, the traditional phenotyping (i.e., manual) is time-consuming and laborious, and the various types of growing structures and limited room for systems hinder phenotyping on a large and high-throughput scale. In this study, a low-cost high-throughput phenotyping system that can be flexibly applied to diverse structures of growing beds with reliable spatial–temporal continuities was developed. The phenotyping system was composed of a low-cost phenotype sensor network with an integrated Raspberry Pi board and camera module. With the distributed camera sensors, the system can provide crop imagery information over the entire growing bed in real time. Furthermore, the modularized image-processing architecture supports the investigation of several phenotypic indices. The feasibility of the system was evaluated for Batavia lettuce grown under different light periods in a container-type plant factory. For the growing lettuces under different light periods, crop characteristics such as fresh weight, leaf length, leaf width, and leaf number were manually measured and compared with the phenotypic indices from the system. From the results, the system showed varying phenotypic features of lettuce for the entire growing period. In addition, the varied growth curves according to the different positions and light conditions confirmed that the developed system has potential to achieve many plant phenotypic scenarios at low cost and with spatial versatility. As such, it serves as a valuable development tool for researchers and cultivators interested in phenotyping.

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.

In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the Republic of Korea, and the spectral data of wet and dry soil were collected. The regression models were compared and analyzed using partial least squares regression (PLSR) with Savitzky–Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing to predict the soil properties. Analysis showed that the predictive regression model of wet soil with SG smoothing and an SNV did not meet the evaluation criteria of a fair model. However, the regression model of dry soil with SG smoothing was fair for clay, pH, EC, and TN at RPD = 1.90, 1.87, 1.60, and 1.43 and R² = 0.79, 0.81, 0.64, and 0.64, respectively, while the regression model of dry soil with an SNV was good for clay, pH, EC, and TN at RPD = 2.21, 1.96, 1.70, and 1.44 and R² = 0.84, 0.81, 0.76, 0.69, respectively. When developing predictive regression models of soil properties, the accuracy for dry soil was higher than that for wet soil, and when applying a single round of preprocessing, the regression model with SNV preprocessing was more accurate than that with SG smoothing.