Search Results (7)

Existing methods for detecting cotton boll diseases frequently exhibit high rates of both false negatives and false positives under complex field conditions (e.g., lighting variations, shadows, and occlusions) and struggle to achieve real-time performance on edge devices. To address these limitations, this study proposes an enhanced YOLOv11n model (YOLOv11n-ECS) for improved detection accuracy. A dataset of cotton boll diseases under different lighting conditions and shooting angles in the field was constructed. To mitigate false negatives and false positives encountered by the original YOLOv11n model during detection, the EMA (efficient multi-scale attention) mechanism is introduced to enhance the weights of important features and suppress irrelevant regions, thereby improving the detection accuracy of the model. Partial Convolution (PConv) is incorporated into the C3k2 module to reduce computational redundancy and lower the model’s computational complexity while maintaining high recognition accuracy. Furthermore, to enhance the localization accuracy of diseased bolls, the original CIoU loss is replaced with Shape-IoU. The improved model achieves floating point operations (FLOPs), parameter count, and model size at 96.8%, 96%, and 96.3% of the original YOLOv11n model, respectively. The improved model achieves an mAP@0.5 of 85.6% and an mAP@0.5:0.95 of 62.7%, representing improvements of 2.3 and 1.9 percentage points, respectively, over the baseline YOLOv11n model. Compared with CenterNet, Faster R-CNN, YOLOv8-LSW, MSA-DETR, DMN-YOLO, and YOLOv11n, the improved model shows mAP@0.5 improvements of 25.7, 21.2, 5.5, 4.0, 4.5, and 2.3 percentage points, respectively, along with corresponding mAP@0.5:0.95 increases of 25.6, 25.3, 8.3, 2.8, 1.8, and 1.9 percentage points. Deployed on a Jetson TX2 development board, the model achieves a recognition speed of 56 frames per second (FPS) and an mAP of 84.2%, confirming its suitability for real-time detection. Furthermore, the improved model effectively reduces instances of both false negatives and false positives for diseased cotton bolls while yielding higher detection confidence, thus providing robust technical support for intelligent cotton boll disease detection. Full article

To identify optimal strategies for high-yield and high-efficiency cultivation under a “short-season direct-seeded cotton with once-only irrigation” regime, we conducted two-year field experiments (2022 and 2023) using a split-plot factorial design with three planting densities (30,000 (D1), 45,000 (D2), and 60,000 (D3) plants·ha⁻¹) and three nitrogen application rates (150 (N1), 180 (N2), and 210 (N3) kg·ha⁻¹). Our study systematically examined how these treatment combinations influenced canopy architecture, physiological traits, yield components, and fiber quality. The results showed that increased planting density significantly enhanced plant height, the leaf area index (LAI), and the number of fruiting branches, with the highest density (D3) contributing to a more compact and efficient canopy. Moderate nitrogen input (N2) significantly increased peroxidase (POD) activity, reduced malondialdehyde (MDA) accumulation, delayed functional leaf senescence, and prolonged the canopy’s photosynthetic performance. A significant interaction between planting density and nitrogen application was observed. The D3N2 treatment (high density with moderate nitrogen) consistently achieved the highest fruiting branch count, boll number per plant, and yields of both seed cotton and lint in both years, while maintaining stable fiber quality. This indicates its strong capacity to balance high yield with quality and maintain physiological resilience. By contrast, the D1N1 treatment (low density and low nitrogen) exhibited a loose canopy, premature photosynthetic decline, and the lowest yield. The D3N3 treatment (high density and high nitrogen) promoted vigorous early growth but reduced stress tolerance during later growth stages, leading to yield instability. These findings demonstrate that moderately increasing planting density while maintaining appropriate nitrogen levels can effectively optimize canopy structure, improve stress resilience, and enhance yield under short-season direct-seeded cotton systems with once-only irrigation. This provides both theoretical underpinning and practical guidance for achieving stable and efficient cotton production under such systems. Full article

Nowadays, cotton boll detection techniques are becoming essential for weaving and textile industries based on the production of cotton. There are limited techniques developed to segment, detect, and count cotton bolls precisely. This analysis identified several limitations and issues with these techniques, including their complex structure, low performance, time complexity, poor quality data, and so on. A proposed technique was developed to overcome these issues and enhance the performance of the detection and counting of cotton bolls. Initially, data were gathered from the dataset, and a pre-processing stage was performed to enhance image quality. An adaptive Gaussian–Wiener filter (AGWF) was utilized to remove noise from the acquired images. Then, an improved Harris Hawks arithmetic optimization algorithm (IH²AOA) was used for segmentation. Finally, an anchor-free compact central attention cotton boll detection network (A-frC²AcbdN) was utilized for cotton boll detection and counting. The proposed technique utilized an annotated dataset extracted from weakly supervised cotton boll detection and counting, aiming to enhance the accuracy and efficiency in identifying and quantifying cotton bolls in the agricultural domain. The accuracy of the proposed technique was 94%, which is higher than that of other related techniques. Similarly, the precision, recall, F1-score, and specificity of the proposed technique were 93.8%, 92.99%, 93.48%, and 92.99%, respectively. Full article

This study aims to enhance the detection accuracy and efficiency of cotton bolls in complex natural environments. Addressing the limitations of traditional methods, we developed an automated detection system based on computer vision, designed to optimize performance under variable lighting and weather conditions. We introduced COTTON-YOLO, an improved model based on YOLOv8n, incorporating specific algorithmic optimizations and data augmentation techniques. Key innovations include the C2F-CBAM module to boost feature recognition capabilities, the Gold-YOLO neck structure for enhanced information flow and feature integration, and the WIoU loss function to improve bounding box precision. These advancements significantly enhance the model’s environmental adaptability and detection precision. Comparative experiments with the baseline YOLOv8 model demonstrated substantial performance improvements with COTTON-YOLO, particularly a 10.3% increase in the AP₅₀ metric, validating its superiority in accuracy. Additionally, COTTON-YOLO showed efficient real-time processing capabilities and a low false detection rate in field tests. The model’s performance in static and dynamic counting scenarios was assessed, showing high accuracy in static cotton boll counting and effective tracking of cotton bolls in video sequences using the ByteTrack algorithm, maintaining low false detections and ID switch rates even in complex backgrounds. Full article

The application of sound wave technology to different plant species has revealed that variations in the Hz, sound pressure intensity, treatment duration, and type of setup of the sound source significantly impact the plant performance. A study conducted on cotton plants treated with Plant Acoustic Frequency Technology (PAFT) highlighted improvements across various growth metrics. In particular, the treated samples showed increases in the height, size of the fourth expanded leaf from the final one, count of branches carrying bolls, quantity of bolls, and weight of individual bolls. Another study showed how the impact of a 4 kHz sound stimulus positively promoted plant drought tolerance. In other cases, such as in transgenic rice plants, GUS expression was upregulated at 250 Hz but downregulated at 50 Hz. In the same way, sound frequencies have been found to enhance the osmotic potential, with the highest observed in samples treated with frequencies of 0.5 and 0.8 kHz compared to the control. Furthermore, a sound treatment with a frequency of 0.4 kHz and a sound pressure level (SPL) of 106 dB significantly increased the paddy rice germination index, as evidenced by an increase in the stem height and relative fresh weight. This paper presents a complete, rationalized and updated review of the literature on the effects of sound waves on the physiology and growth parameters of sound-treated plants. Full article

Under complex field conditions, robust and efficient boll detection at maturity is an important tool for pre-harvest strategy and yield prediction. To achieve automatic detection and counting of long-staple cotton in a natural environment, this paper proposes an improved algorithm incorporating deformable convolution and attention mechanism, called YOLO-C, based on YOLOv7: (1) To capture more detailed and localized features in the image, part of the 3 × 3 convolution in the ELAN layer of the backbone is replaced by deformable convolution to improve the expressiveness and accuracy of the model. (2) To suppress irrelevant information, three SENet modules are introduced after the backbone to improve the ability of feature maps to express information, and CBAM and CA are introduced for comparison experiments. (3) A WIoU loss function based on a dynamic non-monotonic focusing mechanism is established to reduce the harmful gradients generated by low-quality examples on the original loss function and improve the model performance. During the model evaluation, the model is compared with other YOLO series and mainstream detection algorithms, and the model mAP@0.5 achieves 97.19%, which is 1.6% better than the YOLOv7 algorithm. In the model testing session, the root mean square error and coefficient of determination (${\mathrm{R}}^2$) of YOLO-C are 1.88 and 0.96, respectively, indicating that YOLO-C has higher robustness and reliability for boll target detection in complex environments and can provide an effective method for yield prediction of long-staple cotton at maturity. Full article

The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approaches have been implemented to perform phenotypic trait measurement from images for various crops, but few studies have been conducted to count cotton bolls from field images. Supervised learning models require a vast number of annotated images for training, which has become a bottleneck for machine learning model development. The goal of this study is to develop both fully supervised and weakly supervised deep learning models to segment and count cotton bolls from proximal imagery. A total of 290 RGB images of cotton plants from both potted (indoor and outdoor) and in-field settings were taken by consumer-grade cameras and the raw images were divided into 4350 image tiles for further model training and testing. Two supervised models (Mask R-CNN and S-Count) and two weakly supervised approaches (WS-Count and CountSeg) were compared in terms of boll count accuracy and annotation costs. The results revealed that the weakly supervised counting approaches performed well with RMSE values of 1.826 and 1.284 for WS-Count and CountSeg, respectively, whereas the fully supervised models achieve RMSE values of 1.181 and 1.175 for S-Count and Mask R-CNN, respectively, when the number of bolls in an image patch is less than 10. In terms of data annotation costs, the weakly supervised approaches were at least 10 times more cost efficient than the supervised approach for boll counting. In the future, the deep learning models developed in this study can be extended to other plant organs, such as main stalks, nodes, and primary and secondary branches. Both the supervised and weakly supervised deep learning models for boll counting with low-cost RGB images can be used by cotton breeders, physiologists, and growers alike to improve crop breeding and yield estimation. Full article