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Stress by unfavorable environmental conditions, including temperature, light intensity, and photoperiod, significantly impact early-stage growth in crops, such as cucumber seedlings, often resulting in yield reduction and quality degradation. Advanced machine learning (ML) models combined with image-based analysis offer promising solutions for precise, non-invasive stress monitoring. This study aims to classify environmental stress symptom levels in cucumber seedlings using ML models by extracting critical color, texture, and morphological features from RGB images. In a controlled plant factory setup, two-week-old cucumber seedlings were subjected to varied environmental conditions across five chambers with differing temperatures (15, 20, 25, and 30 °C), light intensities (50, 250, and 450 µmol m−2 s−1), and day-night cycles (8/16, 10/14, and 16/8 h). A cost-effective RGB camera, integrated with a microcontroller, captured images from the top of the seedlings over a two-week period, from which sequential forward floating selection (SFFS) and correlation matrices were used to streamline feature extraction. Four ML classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), were trained to detect stress symptoms based on selected features, highlighting that stress symptoms were detectable after day 4. KNN achieved the highest accuracy at 0.94 (94%), followed closely by SVM and RF, both at 93%, while NB reached 88%. Findings suggested that color and texture features were critical indicators of stress, and that the KNN model, with optimized hyperparameters, provided a reliable classification for stress symptom monitoring for seedlings under controlled environments. This study highlights the potential of ML-driven stress symptom detection models for controlled seedling production, enabling real-time decision-making to optimize crop health and productivity. [ABSTRACT FROM AUTHOR]

The agricultural sector faces challenges due to climate change and the growing global population. Alkaline stress could adversely impact plant growth and crop production. This stressor diminishes water quality essential for crop cultivation, consequently impairing plant growth and overall productivity. Gamma-aminobutyric acid (GABA), a non-protein amino acid, may exhibit multifaceted roles in plant responses to adverse environmental conditions. Optimization of crop production in controlled environmental agriculture under artificial light attracted much attention. In the present study, we investigated the effects of different light spectra and GABA concentrations on Lollo Rosso lettuce plants under alkaline stress. Seedlings were placed under different light spectra [red (R), blue (B), white (W), 3R:1B, and 2R:1B:1far red (FR)] and treated with GABA (0 and 50 µmol). Alkaline conditions were instituted by applying NaHCO3 at a concentration of 40 mM. The experiment incorporated a control treatment without NaHCO3. In general, R light exposure caused the highest growth performance of lettuce plants. GABA augmented biomass production in control and alkaline-treated plants across all light spectra. Additionally, it increased the concentrations of photosynthetic and protective pigments under alkaline conditions. GABA also improved the photosynthetic performance under all light spectra. Non-photochemical quenching and pigmentation were enhanced by exposure to B light, especially in plants treated with GABA. Alkaline stress induced an increase in soluble carbohydrate content in the lettuce leaves. These findings highlight the support of GABA application in facilitating coping plants to alkaline stress. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) indicates if a cultivation area is suitable for planting. Currently, site-specific PET models that are based on large geographic regions are vulnerable to inaccurate predictions as a result of climate change and sudden changes in the environmental abiotic stressors that affect plant growth. For the aim of promoting the papaya Sinta F1 cultivar, the study optimized the standard Thornthwaite PET model by integrating three advanced physics-based metaheuristics and evolutionary computing, namely atom search (ASO), differential evolution (DE), and multiverse (MVO) optimizers. The PET value was optimized through minimization as a function of air temperature, light intensity, heat index, and extended heat index. As the PET value approaches 0, it indicates that there is more soil-water content that can be absorbed by plants. Based on the four cultivation treatments (uncontrolled, ASO, DE, and MVO) exposed in three replicates within 90 days, the ASO-optimized Thornthwaite PET-treated (ASOTh) papaya plants resulted in the highest chlorophyll a and b concentrations, densest stomatal density, concentrated root and stem xylem and phloem vessels, considerable root and stem length, most formed leaf count, and strongest action potentials coming from stem membrane for both light and dark periods. This proves the applicability of the intelligent process in modifying the Thornthwaite model for plant growth promotion. Also, through the developed ASOTh, the stem length and thickness ratio was improved for mechanical stability to facilitate more branching leaves and potential fruits during the fruiting stage, and the chlorophyll a and b ratio was enhanced, which naturally extended the light energy band for photosynthesis. Overall, the newly developed ASOTh model may be used to grow papaya seedlings year-round anywhere on Earth if there is a control system to regulate the environmental setting inside the growth chamber. [ABSTRACT FROM AUTHOR]

The foundation of effectively predicting plant disease in the early stage using deep learning algorithms is ideal for addressing food insecurity, inevitably drawing researchers and agricultural specialists to contribute to its effectiveness. The input preprocessor, abnormalities of the data (i.e., incomplete and nonexistent features, class imbalance), classifier, and decision explanation are typical components of a plant disease detection pipeline based on deep learning that accepts an image as input and outputs a diagnosis. Data sets related to plant diseases frequently display a magnitude imbalance due to the scarcity of disease outbreaks in real field conditions. This study examines the effects of several preprocessing methods and class imbalance approaches and deep learning classifiers steps in the pipeline for detecting plant diseases on our data set. We notably want to evaluate if additional preprocessing and effective handling of data inconsistencies in the plant disease pipeline may considerably assist deep learning classifiers. The evaluation's findings indicate that contrast limited adaptive histogram equalization (CLAHE) combined with image sharpening and generative adversarial networks (GANs)-based approach for resampling performed the best among the preprocessing and resampling techniques, with an average classification accuracy of 97.69% and an average F1-score of 97.62% when fed through a ResNet-50 as the deep learning classifier. Lastly, this study provides a general workflow of a disease detection system that allows each component to be individually focused on depending on necessity. [ABSTRACT FROM AUTHOR]

The production of food crops in controlled environment agriculture (CEA) can help mitigate food insecurity that may result from increasingly frequent and severe weather events in agricultural areas. Lighting is an absolute requirement for crop growth in CEA, and is undergoing rapid advances with the advent of tunable, light emitting diode (LED) systems. The integration of these systems into existing CEA environmental control architectures is in its infancy and would benefit from a non-invasive, rapid, real-time, remote sensor that could track crop growth under different lighting regimes. A newly-developed remote chlorophyll a fluorescence (ChlF) sensing device is described herein that provides direct, remote, real-time physiological data collection for integration into tunable LED lighting control systems, thereby enabling better control of crop growth and energy efficiency. Data collected by this device can be used to accurately model growth of red lettuce plants. In addition to monitoring growth, this system can predict relative growth rates (RGR), net assimilation rates (NAR), plant area (PA), and leaf area ratio (LAR).

Unlike in soil culture, a substrate (nutrient solution) in a hydroponics system can flow, and this can affect both nutrient uptake and plant growth. In this study, we hydroponically cultivated Swiss chard (Beta vulgaris L. ssp. cicla) under different flow rates to analyze changes in the growth, nutrient uptake, and nutrient use efficiency. When the flow rate was intensified from 2 to 4 L/min, leaf area, the fresh weight, dry weight, and root length increased. However, when the flow rate was increased from 4 to 8 L/min, values of these growth parameters decreased. The nutrient uptake had a similar trend relative to the growth parameters and nutrient use efficiency of macronutrient elements, increased as the flow rate increased. This indicates that the flow rate affects plant growth by influencing the nutrient uptake, and an increase in the flow rate can aid in improving nutrient use efficiency. In hydroponics, regulating the flow rate at a reasonable volume is recommended to increase yield by enhancing nutrient use efficiency, but too intensive a flow rate may cause excessive physical stimulation to plants and inhibit their growth. Therefore, it is important to choose an appropriate substrate flow rate for optimal hydroponics production. [ABSTRACT FROM AUTHOR]

Watermelon is cultivated worldwide and is mainly grafted onto interspecific squash rootstocks. Light-emitting diodes (LEDs) can be implemented as light sources during indoor production of both species and their spectral quality is of great importance. The objective of the present study was to determine the optimal emission of LEDs with wide wavelength for the production of watermelon and interspecific squash seedlings in a growth chamber. Conditions were set at 22/20 °C temperature (day/night), 16 h photoperiod, and 85 ± 5 μmol m−2 s−1 photosynthetic photon flux density. Illumination was provided by fluorescent (FL, T0) lamps or four LEDs (T1, T2, T3, and T4) emitting varying wide spectra. Watermelon seedlings had greater shoot length, stem diameter, cotyledon area, shoot dry weight-to-length (DW/L) ratio, and Dickson's quality index (DQI) under T1 and T3, while leaf area and shoot dry weight (DW) had higher values under T1. Interspecific squash seedlings had greater stem diameter, and shoot and root DW under T1 and T3, while leaf and cotyledon areas were favored under T1. In both species, T0 showed inferior development. It could be concluded that a light source with high red emission, relatively low blue emission, and a red:far-red ratio of about 3 units seems ideal for the production of high-quality watermelon (scion) and interspecific squash (rootstock) seedlings. [ABSTRACT FROM AUTHOR]

The production of food crops in controlled environment agriculture (CEA) can help mitigate food insecurity that may result from increasingly frequent and severe weather events in agricultural areas. Lighting is an absolute requirement for crop growth in CEA, and is undergoing rapid advances with the advent of tunable, light emitting diode (LED) systems. The integration of these systems into existing CEA environmental control architectures is in its infancy and would benefit from a non-invasive, rapid, real-time, remote sensor that could track crop growth under different lighting regimes. A newly-developed remote chlorophyll a fluorescence (ChlF) sensing device is described herein that provides direct, remote, real-time physiological data collection for integration into tunable LED lighting control systems, thereby enabling better control of crop growth and energy efficiency. Data collected by this device can be used to accurately model growth of red lettuce plants. In addition to monitoring growth, this system can predict relative growth rates (RGR), net assimilation rates (NAR), plant area (PA), and leaf area ratio (LAR). [ABSTRACT FROM AUTHOR]