Environmental factors such as temperature, humidity, light, and CO₂ influence plant growth, and unfavorable environmental conditions cause stress in plants, producing symptoms in their early growth stages. The increasing importance of optimizing crop management strategies has led to a rising demand for the precise evaluation of stress symptoms during early plant growth. Advanced technologies are transforming plant health monitoring through enabling image-based stress analysis. Machine learning (ML) models can effectively identify the important features and morphological changes connected with various stress conditions through the use of large datasets acquired from high-resolution plant images. Therefore, the objective of this study was to develop a method for classifying the early-stage stress symptoms of pepper seedlings and enabling their identification and quantification using image processing and a support vector machine (SVM). Two-week-old pepper seedlings were grown under different temperatures (20, 25, and 30 °C), light intensity levels (50, 250, and 450 µmol m⁻²s⁻¹), and day–night hours (8/16, 10/14, and 16/8) in five controlled plant growth chambers. Images of the seedling canopies were captured daily using a low-cost red, green, and blue (RGB) camera over a two-week period. Eighteen color features, nine texture features using the gray-level co-occurrence matrix (GLCM), and one morphological feature were extracted from each image. A two-way ANOVA and multiple mean comparison (Duncan) analysis were used to determine the statistical significance of the treatment effects. To reduce feature overlap, sequential feature selection (SFS) was applied, and a support vector machine (SVM) was used for stress classification. The SFS method was used to identify the optimal features for the classification model, leading to substantial increases in stress classification accuracy. The SVM model, using these selected features, achieved a classification accuracy of 82% without the SFS and 86% with the SFS. To address overfitting, 5- and 10-fold cross-validation were used, resulting in MAEs of 0.138 and 0.163 for the polynomial kernel, respectively. The SVM model, evaluated with the ROC curve and confusion matrix, achieved a classification accuracy of 85%. This classification approach enables real-time stress monitoring, allowing growers to optimize environmental conditions and enhance seedling growth. Future directions include integrating this system into automated cultivation environments to enable continuous, efficient stress monitoring and response, further improving crop management and productivity.

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm⁻¹, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm⁻¹. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems.

Stem removal from harvested fruits remains one of the most labor-intensive tasks in fruit harvesting, directly affecting the fruit quality and marketability. Accurate and rapid fruit and stem segmentation techniques are essential for automating this process. This study proposes an enhanced You Only Look Once (YOLO) model, AppleStem (AS)-YOLO, which uses a ghost bottleneck and global attention mechanism to segment apple stems. The proposed model reduces the number of parameters and enhances the computational efficiency using the ghost bottleneck while improving feature extraction capabilities using the global attention mechanism. The model was evaluated using both a custom-built and an open dataset, which will be later released to ensure reproducibility. Experimental results demonstrated that the AS-YOLO model achieved high accuracy, with a mean average precision (mAP)@50 of 0.956 and mAP@50–95 of 0.782 across all classes, along with a real-time inference speed of 129.8 frames per second (FPS). Compared with state-of-the-art segmentation models, AS-YOLO exhibited superior performance. The proposed AS-YOLO model demonstrates the potential for real-time application in automated fruit-harvesting systems, contributing to the advancement of agricultural automation.

Understanding the diverse environmental influences on seedling growth is critical for maximizing yields. The need for a more comprehensive understanding of how various environmental factors affect seedling growth is required. Integrating sensor data and image processing techniques offers a promising approach to accurately detect stress symptoms and uncover hidden patterns, enhancing the comprehension of seedling responses to environmental factors. The objective of this study was to quantify environmental stress symptoms for six seedling varieties using image-extracted feature characteristics. Three sensors were used: an RGB camera for color, shape, and size information; a thermal camera for measuring canopy temperature; and a depth camera for providing seedling height from the image-extracted features. Six seedling varieties were grown under controlled conditions, with variations in temperature, light intensity, nutrients, and water supply, while daily automated imaging was conducted for two weeks. Key seedling features, including leaf area, leaf color, seedling height, and canopy temperature, were derived through image processing techniques. These features were then employed to quantify stress symptoms for each seedling type. The analysis of stress effects on the six seedling varieties revealed distinct responses to environmental stressors. Integration of color, size, and shape parameters established a visual hierarchy: pepper and pak choi seedlings showed a good response, cucumber seedlings showed a milder response, and lettuce and tomato seedlings displayed an intermediate response. Pepper and tomato seedlings exhibited a wide range of growth stress symptoms, at 13.00% to 83.33% and 2.96% to 70.01%, respectively, indicating considerable variability in their reactions to environmental stressors. The suggested classification approach provides valuable groundwork for advancing stress monitoring and enabling growers to optimize environmental conditions.

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.

Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R² up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity.