Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management

Recent Advances in Rapid Detection Techniques for Pesticide Residue: A Review

As an important chemical pollutant affecting the safety of agricultural products, the on-site and efficient detection of pesticide residues has become a global trend and hotspot in research. These methodologies were developed for simplicity, high sensitivity, and multiresidue detection. This review introduces the currently available technologies based on electrochemistry, optical analysis, biotechnology, and some innovative and novel technologies for the rapid detection of pesticide residues, focusing on the characteristics, research status, and application of the most innovative and novel technologies in the past 10 years, and analyzes challenges and future development prospects. The current review could be a good reference for researchers to choose the appropriate research direction in pesticide residue detection

Production of a Monoclonal Antibody for the Detection of Forchlorfenuron: Application in an Indirect Enzyme-Linked Immunosorbent Assay and Immunochromatographic Strip

In this study, a monoclonal antibody (mAb) specific to forchlorfenuron (CPPU) with high sensitivity and specificity was produced and designated (9G9). To detect CPPU in cucumber samples, an indirect enzyme-linked immunosorbent assay (ic-ELISA) and a colloidal gold nanobead immunochromatographic test strip (CGN-ICTS) were established using 9G9. The half-maximal inhibitory concentration (IC50) and the LOD for the developed ic-ELISA were determined to be 0.19 ng/mL and 0.04 ng/mL in the sample dilution buffer, respectively. The results indicate that the sensitivity of the antibodies prepared in this study (9G9 mAb) was higher than those reported in the previous literature. On the other hand, in order to achieve rapid and accurate detection of CPPU, CGN-ICTS is indispensable. The IC50 and the LOD for the CGN-ICTS were determined to be 27 ng/mL and 6.1 ng/mL. The average recoveries of the CGN-ICTS ranged from 68 to 82%. The CGN-ICTS and ic-ELISA quantitative results were all confirmed by liquid chromatography—tandem mass spectrometry (LC-MS/MS) with 84–92% recoveries, which indicated the methods developed herein are appropriate for detecting CPPU in cucumber. The CGN-ICTS method is capable of both qualitative and semiquantitative analysis of CPPU, which makes it a suitable alternative complex instrument method for on-site detection of CPPU in cucumber samples since it does not require specialized equipment

Production of a Monoclonal Antibody for the Detection of Forchlorfenuron: Application in an Indirect Enzyme-Linked Immunosorbent Assay and Immunochromatographic Strip

In this study, a monoclonal antibody (mAb) specific to forchlorfenuron (CPPU) with high sensitivity and specificity was produced and designated (9G9). To detect CPPU in cucumber samples, an indirect enzyme-linked immunosorbent assay (ic-ELISA) and a colloidal gold nanobead immunochromatographic test strip (CGN-ICTS) were established using 9G9. The half-maximal inhibitory concentration (IC50) and the LOD for the developed ic-ELISA were determined to be 0.19 ng/mL and 0.04 ng/mL in the sample dilution buffer, respectively. The results indicate that the sensitivity of the antibodies prepared in this study (9G9 mAb) was higher than those reported in the previous literature. On the other hand, in order to achieve rapid and accurate detection of CPPU, CGN-ICTS is indispensable. The IC50 and the LOD for the CGN-ICTS were determined to be 27 ng/mL and 6.1 ng/mL. The average recoveries of the CGN-ICTS ranged from 68 to 82%. The CGN-ICTS and ic-ELISA quantitative results were all confirmed by liquid chromatography—tandem mass spectrometry (LC-MS/MS) with 84–92% recoveries, which indicated the methods developed herein are appropriate for detecting CPPU in cucumber. The CGN-ICTS method is capable of both qualitative and semiquantitative analysis of CPPU, which makes it a suitable alternative complex instrument method for on-site detection of CPPU in cucumber samples since it does not require specialized equipment

The synthesis of new chromogenic sensors containing the thiourea and selective detection for F−, H2PO4−, and Ac− anions

Two new chromogenic sensors 1-(2-hydroxyphenyl)-3-(4-nitrophenyl)thiourea 1 and 1-(3-hydroxypyridin-2-yl)-3-(4-nitrophenyl)thiourea 2 bearing nitrophenyl and thiourea groups were designed and synthesized by one-step procedure and characterized through 1H NMR, 13C NMR, FTIR, and MS. The anion recognition property of the receptors was studied via naked-eye detection, UV–vis as well as 1H NMR. Base on the existence of amino gen and hydroxyl moieties in receptors, receptors 1 and 2 exhibit obvious selectivity by the redshift of UV–vis signals, color changes through naked-eye detection and binding effects for F-, H2PO4- and Ac-. Surprisingly, the detection limit of receptor 1 for F- and Ac- were calculated to be 5.4510-7 M-1 and 2.1110-7 M-1, which indicated that F- and Ac- can be identified with high sensitivity by receptor 1. Besides, simple “test stripes” were developed and as sensors for recognition of F-, H2PO4- or Ac- in DMSO solution. At last, the mechanisms of the recognition process were studied through DFT calculation and 1H NMR titration.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

Early recognition of fruit body diseases in edible fungi can effectively improve the quality and yield of edible fungi. This study proposes a method based on improved ShuffleNetV2 for edible fungi fruit body disease recognition. First, the ShuffleNetV2+SE model is constructed by deeply integrating the SE module with the ShuffleNetV2 network to make the network pay more attention to the target area and improve the model’s disease classification performance. Second, the network model is optimized and improved. To simplify the convolution operation, the 1 × 1 convolution layer after the 3 × 3 depth convolution layer is removed, and the ShuffleNetV2-Lite+SE model is established. The experimental results indicate that the accuracy, precision, recall, and Macro-F1 value of the ShuffleNetV2-Lite+SE model on the test set are, respectively, 96.19%, 96.43%, 96.07%, and 96.25%, which are 4.85, 4.89, 3.86, and 5.37 percent higher than those before improvement. Meanwhile, the number of model parameters and the average iteration time are 1.6 MB and 41 s, which is 0.2 MB higher and 4 s lower than that before the improvement, respectively. Compared with the common lightweight convolutional neural networks MobileNetV2, MobileNetV3, DenseNet, and EfficientNet, the proposed model achieves higher recognition accuracy, and its number of model parameters is significantly reduced. In addition, the average iteration time is reduced by 37.88%, 31.67%, 33.87%, and 42.25%, respectively. The ShuffleNetV2-Lite+SE model proposed in this paper has a good balance among performance, number of parameters, and real-time performance. It is suitable for deploying on resource-limited devices such as mobile terminals and helps in realization of real-time and accurate recognition of fruit body diseases of edible fungi

Growth, Stratification, and Liberation of Phosphorus-Rich C2S in Modified BOF Steel Slag

Basic oxygen furnace (BOF) slag was modified by adding 3.5% SiO2 and holding at 1673 K for 0, 5, 40, 90, 240, or 360 min. Kilo-scale modification was also carried out. The growth, stratification, and liberation of P-rich C2S in the modified slag were investigated. The optimum holding time was 240 min, and 90% of C2S grains were above 30 μm in size. The phosphorus content increased with holding time, and after modification, the phosphorus content in C2S was nearly three times higher than that in the original slag (2.23%). Obvious stratification of C2S was observed in the kilo-scale modification. Upper C2S particles with a relatively larger size of 20–110 μm was independent of RO (FeO-MgO-MnO solid solution) and spinel, which is favorable for liberation. Lower C2S was less than 3 μm and was embedded in spinel, which is not conducive to liberation. The content of phosphorus in upper C2S (6.60%) was about twice that of the lower (3.80%). After grinding, most of the upper C2S existed as free particles and as locked particles in the lower. The liberation degree of C2S in the upper increased with grinding time, from 86.02% to 95.92% in the range of 30–300 s, and the optimum grinding time was 180 s. For the lower slag grinding for 300 s, the liberation degree of C2S was 40.07%

Maize Seedling Leave Counting Based on Semi-Supervised Learning and UAV RGB Images

The number of leaves in maize seedlings is an essential indicator of their growth rate and status. However, manual counting of seedlings is inefficient and limits the scope of the investigation. Deep learning has shown potential for quickly identifying seedlings, but it requires larger, labeled datasets. To address these challenges, we proposed a method for counting maize leaves from seedlings in fields using a combination of semi-supervised learning, deep learning, and UAV digital imagery. Our approach leveraged semi-supervised learning and novel methods for detecting and counting maize seedling leaves accurately and efficiently. Specifically, we used a small amount of labeled data to train the SOLOv2 model based on the semi-supervised learning framework Noisy Student. This model can segment complete maize seedlings from UAV digital imagery and generate foreground images of maize seedlings with background removal. We then trained the YOLOv5x model based on Noisy Student with a small amount of labeled data to detect and count maize leaves. We divided our dataset of 1005 images into 904 training images and 101 testing images, and randomly divided the 904 training images into four sets of labeled and unlabeled data with proportions of 4:6, 3:7, 2:8, and 1:9, respectively. The results indicated that the SOLOv2 Resnet101 outperformed the SOLOv2 Resnet50 in terms of segmentation performance. Moreover, when the labeled proportion was 30%, the student model SOLOv2 achieved a similar segmentation performance to the fully supervised model with a mean average precision (mAP) of 93.6%. When the labeled proportion was 40%, the student model YOLOv5x demonstrated comparable leaf counting performance to the fully supervised model. The model achieved an average precision of 89.6% and 57.4% for fully unfolded leaves and newly appearing leaves, respectively, with counting accuracy rates of 69.4% and 72.9%. These results demonstrated that our proposed method based on semi-supervised learning and UAV imagery can advance research on crop leaf counting in fields and reduce the workload of data annotation