A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

Pest detection in dynamic environments: an adaptive continual test-time domain adaptation strategy

Pest management is essential for agricultural production and food security, as pests can cause significant crop losses and economic impact. Early pest detection is key to timely intervention. While object detection models perform well on various datasets, they assume i.i.d. data, which is often not the case in diverse real-world environments, leading to decreased accuracy. To solve the problem, we propose the CrossDomain-PestDetect (CDPD) method, which is based on the YOLOv9 model and incorporates a test-time adaptation (TTA) framework. CDPD includes Dynamic Data Augmentation (DynamicDA), a Dynamic Adaptive Gate (DAG), and a Multi-Task Dynamic Adaptation Model (MT-DAM). Our DynamicDA enhances images for each batch by combining strong and weak augmentations. The MT-DAM integrates an object detection model with an image segmentation model, exchanging information through feature fusion at the feature extraction layer. During testing, test-time adaptation updates both models, continuing feature fusion during forward propagation. DAG adaptively controls the degree of feature fusion to improve detection capabilities. Self-supervised learning enables the model to adapt during testing to changing environments. Experiments show that without test-time adaptation, our method achieved a 7.6% increase in mAP50 over the baseline in the original environment and a 16.1% increase in the target environment. Finally, with test-time adaptation, the mAP50 score in the unseen target environment reaches 73.8%, which is a significant improvement over the baseline.

Hybrid feature optimized CNN for rice crop disease prediction

The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Advances in deep learning have greatly enhanced disease diagnostic techniques in agriculture. Accurate identification of rice plant diseases is crucial to preventing the severe consequences these diseases can have on crop yield. Current methods often struggle with reliably diagnosing conditions and detecting issues in leaf images. Previously, leaf segmentation posed challenges, and while analyzing complex disease stages can be effective, it is computationally intensive. Therefore, segmentation methods need to be more accurate, cost-effective, and reliable. To address these challenges, we propose a hybrid bio-inspired algorithm, named the Hybrid WOA_APSO algorithm, which merges Adaptive Particle Swarm Optimization (APSO) with the Whale Optimization Algorithm (WOA). For disease classification in rice crops, we utilize a Convolutional Neural Network (CNN). Multiple experiments are conducted to evaluate the performance of the proposed model using benchmark datasets (Plantvillage), with a focus on feature extraction, segmentation, and preprocessing. Optimizing feature selection is a critical factor in enhancing the classification algorithm's accuracy. We compare the accuracy, sensitivity, and specificity of our model against industry-standard techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and conventional CNN models. The experimental results indicate that the proposed hybrid approach achieves an impressive accuracy of 97.5% (Refer Table 8), which could inspire further research in this field.

Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse

Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red-Green-Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = -0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = -0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = -0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = -0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.

Deep learning-based association analysis of root image data and cucumber yield

The root system is important for the absorption of water and nutrients by plants. Cultivating and selecting a root system architecture (RSA) with good adaptability and ultrahigh productivity have become the primary goals of agricultural improvement. Exploring the correlation between the RSA and crop yield is important for cultivating crop varieties with high-stress resistance and productivity. In this study, 277 cucumber varieties were collected for root system image analysis and yield using germination plates and greenhouse cultivation. Deep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images. The results showed that U-Net can automatically extract cucumber root systems with high quality (F1_score ≥ 0.95), and the trained ResNet50 can predict cucumber yield grade through seedling root system image, with the highest F1_score reaching 0.86 using 10-day-old seedlings. The root angle had the strongest correlation with yield, and the shallow- and steep-angle frequencies had significant positive and negative correlations with yield, respectively. RSA and nutrient absorption jointly affected the production capacity of cucumber plants. The germination plate planting method and automated root system segmentation model used in this study are convenient for high-throughput phenotypic (HTP) research on root systems. Moreover, using seedling root system images to predict yield grade provides a new method for rapidly breeding high-yield RSA in crops such as cucumbers.

scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged. However, the low generalization and high computational cost are the main bottlenecks of existing methods. In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a metaheuristic algorithm (Feature Selection based on Quantum Squirrel Search Algorithm) to extract the optimal gene set, which largely guarantees the performance of cell clustering. We conducted simulation experiments in several aspects to verify the performance of the proposed approach. scFseCluster performed very well on eight benchmark scRNA-seq datasets because of the optimal gene sets obtained using the Feature Selection based on Quantum Squirrel Search Algorithm. The comparative study demonstrated the significant advantages of scFseCluster over seven State-of-the-Art algorithms. In addition, our analysis shows that feature selection on high-variable genes can significantly improve clustering performance. In conclusion, our study demonstrates that scFseCluster is a highly versatile tool for enhancing scRNA-seq data clustering analysis.

Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA's performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA's performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis.

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

Introduction

The identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.

Methods

To address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.

Results and discussion

The MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking.