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Abstract Background To explore the clinical characteristics of ovarian Brenner tumors and provide some basis for the treatment regimen of ovarian Brenner tumors. Methods A retrospective analysis of the pathology database of surgical specimens at the Huzhou Maternal and Child Health Hospital from September 2008 to February 2023 was conducted. Patients who were pathologically diagnosed with ovarian Brenner tumors were included. Clinical data of patients was collected, and their diagnostic and treatment characteristics were summarized and analyzed. Results A total of 20 cases were included in this study, all of which were histologically confirmed by surgical pathology. Among them, 8 cases (40%) were combined with serous, mucinous cystadenoma, or simple cyst. One case presented with a benign ovarian Brenner tumor combined with mucinous cystadenoma, underwent right adnexectomy, and relapsed 5 years later as a malignant Brenner tumor (MBT) coexisting with ovarian squamous cell carcinoma. Multiple tumor markers were elevated malignantly, with CA199 being the most significant. Treatments included unilateral adnexectomy in 7 cases, bilateral adnexectomy in 3 cases, total hysterectomy with bilateral adnexectomy in 7 cases, radical hysterectomy in 1 case, and 2 cases underwent ovarian staging surgery. MBT patients received three cycles of postoperative chemotherapy with the carboplatin-paclitaxel (TC) regimen. Follow-up: One case with concomitant cervical cancer was lost to follow-up after surgery in an external hospital; one case with concomitant ovarian cancer received no further treatment after surgery and was lost to follow-up after 2 years; one case with concomitant endometrial cancer received no further treatment after surgery, and had no recurrence after 4 years of follow-up. Regular follow-up for MBT patients continued for 5 years without recurrence. The remaining 16 cases were followed up for a period ranging from 6 months to 7 years, with no reported recurrences. Conclusion Clinical manifestations and auxiliary examinations of ovarian Brenner tumors lack obvious specificity. When necessary, a combination of tumor markers and imaging examinations can aid in diagnosis. Surgical strategies should be selected according to the patient's menopausal status. Trial registration Not applicable.

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management in maize fields. Among various navigation techniques, visual navigation using widely available RGB images is a cost-effective solution. However, current mainstream methods for maize crop row detection often rely on highly specialized, manually devised heuristic rules, limiting the scalability of these methods. To simplify the solution and enhance its universality, we propose an innovative crop row annotation strategy. This strategy, by simulating the strip-like structure of the crop row's central area, effectively avoids interference from lateral growth of crop leaves. Based on this, we developed a deep learning network with a dual-branch architecture, InstaCropNet, which achieves end-to-end segmentation of crop row instances. Subsequently, through the row anchor segmentation technique, we accurately locate the positions of different crop row instances and perform line fitting. Experimental results demonstrate that our method has an average angular deviation of no more than 2°, and the accuracy of crop row detection reaches 96.5%.

Weed competitiveness monitoring is crucial for field management at specific locations. Recent research in the fusion of multimodal data from unmanned aerial vehicles (UAVs) has propelled this advancement. However, these studies merely stack extracted features equivalently, neglecting the full utilization of fused information. This study utilizes hyperspectral and LiDAR data collected by UAVs to proposes a multimodal deep fusion model (MulDFNet) using Transformer and multi-layer residuals. It utilizes a comprehensive competitive index (CCI-A) based on multidimensional phenotypes of maize to assess the competitiveness of weeds in farmland ecosystems. To validate the effectiveness of this model, a series of ablation studies were conducted involving different modalities data, with/without the Transformer Encoder (TE) modules, and different fusion modules (shallow residual fusion module, deep feature fusion module). Additionally, a comparison was made with early/late stacking fusion models, traditional machine learning models, and deep learning models from relevant studies. The results indicate that the multimodal deep fusion model utilizing HSI, VI, and CHM data achieved a predictive effect of R2 = 0.903 (RMSE = 0.078). Notably, the best performance was observed during the five-leaf stage. The combination of shallow and deep fusion modules demonstrated better predictive performance compared to a single fusion module. The positive impact of the TE module on model performance is evident, as its multi-head attention mechanism aids in better capturing the relationships and importance between feature maps and competition indices, thereby enhancing the model's predictive capability. In weed competition prediction, the multimodal deep fusion model proposed in this study has demonstrated significantly better predictive performance compared to early/late stacking fusion models and other machine learning models (RF, SVR, PLS, DNN-F2 and Multi-channel CNN). Overall, the multimodal deep fusion model developed in this study demonstrates outstanding performance in assessing weed competitiveness and can predict the competitive intensity of weeds in maize across various growth stages on a broad scale.

Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R2 of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R2 of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R2) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas.

Herbicide-resistant weeds represent a significant challenge to modern agriculture. The need for innovative and sustainable weed management strategies has become increasingly pressing as the threat of herbicide-resistant weeds continues to escalate. Unmanned aerial vehicles (UAVs) and various sensors have become indispensable tools in plant phenotyping studies. In this study, a comprehensive resistance score (CRS) was proposed to effectively quantify weed resistance in the field. Multimodal data fusion and deep learning were utilized to perform regression of CRS, three different fusion methods for 3D-CNN and 2D-CNN to extract and fuse multimodal information collected by UAVs including spectral, structural, and texture information for weed resistance. Our findings demonstrate that (1) discernible differences in spectral response exist between susceptible and resistant weeds, with the optimal band for the Successive Projections Algorithm (SPA) selection coinciding with the optimal band for resistance expression band; (2) resistance assessment accuracy is enhanced through multimodal data fusion, with the late deep fusion network exhibiting the best accuracy, R2 of 0.777 and RMSE of 0.547; (3) the multimodal fusion network model displays robust adaptability in resistance assessment across varying densities and effectively generates weed resistance map. Overall, this research demonstrates the effectiveness of using multimodal data fusion and CRS, combined with deep learning for achieving accurate and reliable weed resistance assessment in agricultural fields.

Currently, mechanical and chemical damage is the main way to carry out weed control. The use of chlorophyll fluorescence (CF) technology to nondestructively monitor the stress physiological state of weeds is significant to reveal the damage mechanism of mechanical and chemical stresses as well as complex stresses. Under simulated real field environmental conditions, different species and leaf age weeds (Digitaria sanguinalis 2-5 leaf age, and Erigeron canadensis 5-10 leaf age) were subjected to experimental treatments for 1-7 days, and fluorescence parameters were measured every 24 h using a chlorophyll fluorometer. The aim of this study was to investigate the changes in CF parameters of different species of weeds (Digitaria sanguinalis, Erigeron canadensis) at their different stress sites under chemical, mechanical and their combined stresses. The results showed that when weeds (Digitaria sanguinalis and Erigeron canadensis) were chemically stressed in different parts, their leaf back parts were the most severely stressed after 7 days, with photosynthetic inhibition reaching R=75%. In contrast, mechanical stress differs from its changes, and after a period of its stress, each parameter recovers somewhat after 1 to 2 days of stress, with heavy mechanical stress R=11%. Complex stress had the most significant effect on CF parameters, mainly in the timing and efficiency of changes in Fv/Fm, Fq’/Fm’, ETR, Rfd, NPQ and Y(NO), with R reaching 71%-73% after only 3-4 days of complex stress, and its changes in complex stress were basically consistent with the pattern of changes in its chemical stress. The results of the study will help to understand the effects of mechanical and chemical stresses and combined stresses on CF parameters of weeds and serve as a guide for efficient weed control operations and conducting weed control in the future.

Atrazine is one of the most widely used herbicides in weed management. However, the widespread use of atrazine has concurrently accelerated the evolution of weed resistance mechanisms. Resistant weeds were identified early to contribute to crop protection in precision agriculture before visible symptoms of atrazine application to weeds in actual field environments. New developments in unmanned aerial vehicle (UAV) platforms and sensor technologies promote cost-effective data collection by collecting multi-modal data at very high spatial and spectral resolution. In this study, we obtained multispectral and RGB images using UAVs, increased available information with the help of image fusion technology, and developed a weed spectral resistance index, WSRI = (RE-R)/(RE-B), based on the difference between susceptible and resistant weed biotypes. A deep convolutional neural network (DCNN) was applied to evaluate the potential for identifying resistant weeds in the field. Comparing the WSRI introduced in this study with previously published vegetation indices (VIs) shows that the WSRI is better at classifying susceptible and resistant weed biotypes. Fusing multispectral and RGB images improved the resistance identification accuracy, and the DCNN achieved high field accuracies of 81.1% for barnyardgrass and 92.4% for velvetleaf. Time series and weed density influenced the study of weed resistance, with 4 days after application (4DAA) identified as a watershed timeframe in the study of weed resistance, while different weed densities resulted in changes in classification accuracy. Multispectral and deep learning proved to be effective phenotypic techniques that can thoroughly analyze weed resistance dynamic response and provide valuable methods for high-throughput phenotyping and accurate field management of resistant weeds.

The aboveground fresh weight of weeds is an important indicator that reflects their biomass and physiological activity and directly affects the criteria for determining the amount of herbicides to apply. In precision agriculture, the development of models that can accurately locate weeds and predict their fresh weight can provide visual support for accurate, variable herbicide application in real time. In this work, we develop a two-stream dense feature fusion convolutional network model based on RGB-D data for the real-time prediction of the fresh weight of weeds. A data collection method is developed for the compilation and production of RGB-D data sets. The acquired images undergo data enhancement, and a depth transformation data enhancement method suitable for depth data is proposed. The main idea behind the approach in this study is to use the YOLO-V4 model to locate weeds and use the two-stream dense feature fusion network to predict their aboveground fresh weight. In the two-stream dense feature fusion network, DenseNet and NiN methods are used to construct a Dense-NiN-Block structure for deep feature extraction and fusion. The Dense-NiN-Block module was embedded in five convolutional neural networks for comparison, and the best results were achieved with DenseNet201. The test results show that the predictive ability of the convolutional network using RGB-D as the input is better than that of the network using RGB as the input without the Dense-NiN-Block module. The mAP of the proposed network is 75.34% (IoU value of 0.5), the IoU is 86.36%, the detection speed of the fastest model with a RTX2080Ti NVIDIA graphics card is 17.8 fps, and the average relative error is approximately 4%. The model proposed in this paper can provide visual technical support for precise, variable herbicide application. The model can also provide a reference method for the non-destructive prediction of crop fresh weight in the field and can contribute to crop breeding and genetic improvement.

Defoliant spraying is an important aspect of mechanized cotton harvesting. Fully and uniformly spraying defoliant could improve the quality of defoliation and reduce the impurity content in cotton. Improving the coverage of defoliant droplets in the middle and lower layers of cotton and ensuring the full and even dispersion of droplets in the cotton canopy are essentially in increasing the defoliation effect. In this study, we assessed the effect of aviation spray adjuvants on droplet deposition, defoliation, boll opening and defoliant retention in cotton leaves sprayed by an unmanned aerial vehicle (UAV). The results showed that adding aviation spray adjuvants could significantly improve the defoliant droplet deposition. Fifteen days after spraying, the defoliation rate was 80.31% and the boll opening was 90.61%. The defoliation rate increased by 3.12−34.62% and the boll opening rate increased by 6.67−29.56% after the addition of aviation spray adjuvants. Using a vegetable oil adjuvant could significantly increase the droplet coverage rate and the retention of defoliants in cotton leaves.

Unmanned aerial vehicles (UAVs), as emerging plant protection machinery, have the advantages of high operational efficiency, high speed, and low drift. The current study aimed to elucidate the characteristics of droplet distribution and drift, control efficiency on cotton aphids and spider mites, and attachment and absorption of cotton leaves during UAV spraying. Kromekote card and filter paper are used as samplers to collect droplets, and the droplet density, coverage rate, deposition, and drift percentage are statistically analyzed. The pooled results showed that the droplet uniformity, the droplet coverage rate, the deposition, and the drifting ability are higher when the UAV flight height was 2 m. The control effects by UAV spraying on cotton aphids and spider mites were 63.7% and 61.3%, respectively. These values are slightly inferior to those obtained through boom spraying. Cotton leaf attachment and absorption of spirodiclofen after UAV spraying were slightly lower than those after boom spraying, therefore, the control efficiency of cotton pests is slightly different. According to the different flight height operations by the UAV sprayer, the drift capability of the droplets at 2 m flight height was large, and the droplet uniformity and deposition were satisfactory. The research results could provide the theoretical basis and technical support for UAV operation.