The efficiency of photosynthesis in strawberry plants is measured to maintain the quality and quantity of strawberries produced. The latest method used to measure the photosynthetic status of plants is chlorophyll fluorescence imaging (CFI), which has the advantage of obtaining plant spatiotemporal data non-destructively. This study developed a CFI system to measure the maximum quantum efficiency of photochemistry (Fv/Fm). The main components of this system include a chamber for plants to adapt to dark environments, blue LED light sources to excite the chlorophyll in plants, and a monochrome camera with a lens filter attached to capture the emission spectra. In this study, 120 pots of strawberry plants were cultivated for 15 days and divided into four treatment groups: control, drought stress, heat stress, and a combination of drought and heat stress, resulting in Fv/Fm values of 0.802 ± 0.0036, 0.780 ± 0.0026, 0.768 ± 0.0023, and 0.749 ± 0.0099, respectively. A strong correlation was found between the developed system and a chlorophyll meter (r = 0.75). These results prove that the developed CFI system can accurately capture the spatial and temporal dynamics resulting from the response of strawberry plants to abiotic stresses.

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a shortage of expert manpower is also deepening. Fortunately, research on various early diagnosis systems based on deep learning is actively underway to solve these problems, but the problem of lack of diversity in some hard-to-collect disease samples remains. This imbalanced data increases the bias of machine learning models, causing overfitting problems. In this paper, we propose a data augmentation method based on an image-to-image translation model to solve the bias problem by supplementing these insufficient diseased leaf images. The proposed augmentation method performs translation between healthy and diseased leaf images and utilizes attention mechanisms to create images that reflect more evident disease textures. Through these improvements, we generated a more plausible diseased leaf image compared to existing methods and conducted an experiment to verify whether this data augmentation method could further improve the performance of a classification model for early diagnosis of plants. In the experiment, the PlantVillage dataset was used, and the extended dataset was built using the generated images and original images, and the performance of the classification models was evaluated through the test set.

Strawberry (Fragaria × ananassa Duch) plants are vulnerable to climatic change. The strawberry plants suffer from heat and water stress eventually, and the effects are reflected in the development and yields. In this investigation, potential chlorophyll-fluorescence-based indices were selected to detect the early heat and water stress in strawberry plants. The hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm–900 nm. From the hyperspectral cube, the region of interest (leaves) was identified, followed by the extraction of eight chlorophyll-fluorescence indices from the region of interest (leaves). These eight chlorophyll-fluorescence indices were analyzed deeply to identify the best indicators for our objective. The indices were used to develop machine-learning models to assess the performance of the indicators by accuracy assessment. The overall procedure is proposed as a new workflow for determining strawberry plants’ early heat and water stress. The proposed workflow suggests that by including all eight indices, the random-forest classifier performs well, with an accuracy of 94%. With this combination of the potential indices, namely the red-edge vegetation stress index (RVSI), chlorophyll B (Chl-b), pigment-specific simple ratio for chlorophyll B (PSSR_b), and the red-edge chlorophyll index (CI_REDEDGE), the gradient-boosting classifier performs well, with an accuracy of 91%. The proposed workflow works well with a limited number of training samples which is an added advantage.

Ginseng is a perennial herbaceous plant that has been widely consumed for medicinal and dietary purposes since ancient times. Ginseng plants require shade and cool temperatures for better growth; climate warming and rising heat waves have a negative impact on the plants’ productivity and yield quality. Since Republic of Korea’s temperature is increasing beyond normal expectations and is seriously threatening ginseng plants, an early-stage non-destructive diagnosis of stressed ginseng plants is essential before symptomatic manifestation to produce high-quality ginseng roots. This study demonstrated the potential of fluorescence hyperspectral imaging to achieve the early high-throughput detection and prediction of chlorophyll composition in four varieties of heat-stressed ginseng plants: Chunpoong, Jakyeong, Sunil, and Sunmyoung. Hyperspectral imaging data of 80 plants from these four varieties (temperature-sensitive and temperature-resistant) were acquired before and after exposing the plants to heat stress. Additionally, a SPAD-502 meter was used for the non-destructive measurement of the greenness level. In accordance, the mean spectral data of each leaf were extracted from the region of interest (ROI). Analysis of variance (ANOVA) was applied for the discrimination of heat-stressed plants, which was performed with 96% accuracy. Accordingly, the extracted spectral data were used to develop a partial least squares regression (PLSR) model combined with multiple preprocessing techniques for predicting greenness composition in ginseng plants that significantly correlates with chlorophyll concentration. The results obtained from PLSR analysis demonstrated higher determination coefficients of R²_val = 0.90, and a root mean square error (RMSE) of 3.59%. Furthermore, five proposed bands (683 nm, 688 nm, 703 nm, 731 nm, and 745 nm) by stepwise regression (SR) were developed into a PLSR model, and the model coefficients were used to create a greenness-level concentration in images that showed differences between the control and heat-stressed plants for all varieties.

Monitoring the occurrence of plant diseases and pests such as fungi, viruses, nematodes, and insects in crops and collecting environmental information such as temperature, humidity, and light levels is crucial for sustainable greenhouse management. It is essential to control the environment through measures like adjusting vents, using shade nets, and employing screen controls to achieve optimal growing conditions, ensuring the sustainability of the greenhouse. In this paper, an artificial intelligence-based integrated environmental control system was developed to enhance the sustainability of the greenhouse. The system automatically acquires images of crop diseases and augments the disease image information according to environmental data, utilizing deep-learning models for classification and feedback. Specifically, the data are augmented by measuring scattered light within the greenhouse, compensating for potential losses in the images due to variations in light intensity. This augmentation addresses recognition issues stemming from data imbalances. Classifying the data is done using the Faster R-CNN model, followed by a comparison of the accuracy results. This comparison enables feedback for accurate image loss correction based on reflectance, ultimately improving recognition rates. The empirical experimental results demonstrated a 94% accuracy in classifying diseases, showcasing a high level of accuracy in real greenhouse conditions. This indicates the potential utility of employing optimal pest control strategies for greenhouse management. In contrast to the predominant direction of most existing research, which focuses on simply utilizing extensive learning and resources to enhance networks and optimize loss functions, this study demonstrated the performance improvement effects of the model by analyzing video preprocessing and augmented data based on environmental information. Through such efforts, attention should be directed towards quality improvement using information rather than relying on massive data collection and learning. This approach allows the acquisition of optimal pest control timing and methods for different types of plant diseases and pests, even in underdeveloped greenhouse environments, without the assistance of greenhouse experts, using minimal resources. The implementation of such a system will result in a reduction in labor for greenhouse management, a decrease in pesticide usage, and an improvement in productivity.

Introduction: Traditional soil-based cultivation of Centella asiatica L. faces seasonal limitations and quality inconsistency.Methods: This study developed and assessed a smart farm system incorporating Deep Flow Technique (DFT) hydroponics for stable, year-round production with consistent bioactive compound profiles. System development was conducted at the National Institute of Agricultural Sciences, followed by a six-month field assessment at Chungju Agricultural Technology Center, South Korea. The cultivation system comprised a 99 m² greenhouse with a 19.2 m² active hydroponic area, maintaining key environmental parameters.Results: We observed differential bioactive compound accumulation between cultivation tiers. Madecassic acid content was 2.3-fold higher in the LED-equipped tier (1.25 ± 0.04 mg/g) versus the fluorescent-equipped tier (0.54 ± 0.03 mg/g, p<0.001).Discussion: The system demonstrated operational reliability, enabling year-round production and establishing a technological foundation for controlled environment medicinal plant cultivation with standardized compound profiles.