Image Cropping Research Articles

Retracted: A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain

Retracted: A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain

Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors.

The purpose is to assess the performance of a pre-trained deep learning model in the task of classifying between coronavirus disease (COVID)-positive and COVID-negative patients from chest radiographs (CXRs) while considering various image acquisition parameters, clinical factors, and patient demographics. Standard and soft-tissue CXRs of 9860 patients comprised the "original dataset," consisting of training and test sets and were used to train a DenseNet-121 architecture model to classify COVID-19 using three classification algorithms: standard, soft tissue, and a combination of both types of images via feature fusion. A larger more-current test set of 5893 patients (the "current test set") was used to assess the performance of the pretrained model. The current test set contained a larger span of dates, incorporated different variants of the virus and included different immunization statuses. Model performance between the original and current test sets was evaluated using area under the receiver operating characteristic curve (ROC AUC) [95% CI]. The model achieved AUC values of 0.67 [0.65, 0.70] for cropped standard images, 0.65 [0.63, 0.67] for cropped soft-tissue images, and 0.67 [0.65, 0.69] for both types of cropped images. These were all significantly lower than the performance of the model on the original test set. Investigations regarding matching the acquisition dates between the test sets (i.e., controlling for virus variants), immunization status, disease severity, and age and sex distributions did not fully explain the discrepancy in performance. Several relevant factors were considered to determine whether differences existed in the test sets, including time period of image acquisition, vaccination status, and disease severity. The lower performance on the current test set may have occurred due to model overfitting and a lack of generalizability.

Collaborative Multi-Metadata Fusion to Improve the Classification of Lumbar Disc Herniation.

Computed tomography (CT) images are the most commonly used radiographic imaging modality for detecting and diagnosing lumbar diseases. Despite many outstanding advances, computer-aided diagnosis (CAD) of lumbar disc disease remains challenging due to the complexity of pathological abnormalities and poor discrimination between different lesions. Therefore, we propose a Collaborative Multi-Metadata Fusion classification network (CMMF-Net) to address these challenges. The network consists of a feature selection model and a classification model. We propose a novel Multi-scale Feature Fusion (MFF) module that can improve the edge learning ability of the network region of interest (ROI) by fusing features of different scales and dimensions. We also propose a new loss function to improve the convergence of the network to the internal and external edges of the intervertebral disc. Subsequently, we use the ROI bounding box from the feature selection model to crop the original image and calculate the distance features matrix. We then concatenate the cropped CT images, multiscale fusion features, and distance feature matrices and input them into the classification network. Next, the model outputs the classification results and the class activation map (CAM). Finally, the CAM of the original image size is returned to the feature selection network during the upsampling process to achieve collaborative model training. Extensive experiments demonstrate the effectiveness of our method. The model achieved 91.32% accuracy in the lumbar spine disease classification task. In the labelled lumbar disc segmentation task, the Dice coefficient reaches 94.39%. The classification accuracy in the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) reaches 91.82%.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment of CNNs onto resource-constrained embedded devices. To address this issue, we propose, a comprehensive compression framework to reduce the computational overhead of CNNs. In, we first introduce dynamic image cropping, where we design a lightweight foreground predictor to accurately crop the most informative foreground object of input images for inference, which avoids redundant computation on background regions. Subsequently, we present compound shrinking to collaboratively compress the three dimensions (depth, width, and resolution) of CNNs according to their contribution to accuracy and model computation. Dynamic image cropping and compound shrinking together constitute a multi-dimensional CNN compression framework, which is able to comprehensively reduce the computational redundancy in both input images and neural network architectures, thereby improving the inference efficiency of CNNs. Further, we present a dynamic inference framework to efficiently process input images with different recognition difficulties, where we cascade multiple models with different complexities from our compression framework and dynamically adopt different models for different input images, which further compresses the computational redundancy and improves the inference efficiency of CNNs, facilitating the deployment of advanced CNNs onto embedded hardware. Experiments on ImageNet-1K demonstrate that reduces the computation of ResNet-50 by 48.8% while improving the top-1 accuracy by 0.8%. Meanwhile, we improve the accuracy by 4.1% with similar computation compared to HRank. the state-of-the-art compression framework. The source code and models are available at

Cotton Vision: A Machine Learning-Based App for Rapid Diagnosis of Cotton Diseases.

Abstract: This The detrimental impact of bacterial and fungal diseases on cotton crop yields and profitability underscores the urgency for rapid and precise field diagnosis. This paper introduces CottonVision, a pioneering mobile application leveraging deep learning for real-time identification of cotton diseases from images. By enabling farmers to capture smartphone photos of leaves, the system employs a robust convolutional neural network, specifically an Inception-v3 model, trained on a comprehensive dataset of over 2300 cotton crop images. The application swiftly classifies these images into four distinct categories: diseased cotton leaf, diseased cotton plant, Fresh cotton leaf, and Fresh cotton plant. Deployed on Android devices via TensorFlow Lite, the optimized model boasts a remarkable 97% test accuracy. CottonVision serves as an indispensable tool, furnishing farmers with instant diagnostic results crucial for early intervention. By facilitating prompt identification of emerging infections, the application aids in curtailing further spread and implementing timely control measures. The user-friendly interface of CottonVision offers an accessible and practical solution, empowering growers with real-time decision support for efficient disease management in cotton crops.

Archaeological site segmentation of ancient city walls based on deep learning and LiDAR remote sensing

Ancient city walls, one of the most notable distinguishing features of Chinese ancient cities, are military defenses constructed of rammed earth. The ancient city walls have considerable study value because they served as the city's boundary and a symbol of power. However, as a result of natural erosion and human activities, many sites have been ruined. Existing optical remote sensing technologies, LiDAR point cloud processing algorithms, and deep learning methods are inadequate for the extraction and segmentation of ancient city wall sites. The novel semantic segmentation method for ancient city wall sites is described in this paper that extracts sites at the pixel level from LiDAR remote sensing data based on deep learning. To begin, the point cloud data collected by airborne laser scanning is processed into DEM data, and the distribution of ancient city walls in the study area is obtained through archaeological survey and expert interpretation. The dataset for deep learning semantic segmentation is then generated using image cropping and data augmentation techniques. Third, implement a U-Net semantic segmentation framework for microtopographic archaeological sites, and predict ancient city wall sites in the testing region after model training. Finally, the deep learning results are optimized using the connected component analysis method, and prediction mistakes such as holes and noise are removed. Taking Jinancheng, the capital city of the Chu kingdom, as an example, the proposed method process can identify and extract the ancient city wall sites at the pixel level, where the evaluation metrics reach 94.12% (Precision) and 81.38% (IoU). The experiment results are excellent due to improvement strategies in the dataset generation, model training, and post-processing steps. Thus, this study is significant for the current survey and protection of ancient city wall sites. The source code will be freely available at https://github.com/wshunli/Open-CHAI-CityWalls.

Deep learning-based multi-stage postoperative type-b aortic dissection segmentation using global-local fusion learning

Objective. Type-b aortic dissection (AD) is a life-threatening cardiovascular disease and the primary treatment is thoracic endovascular aortic repair (TEVAR). Due to the lack of a rapid and accurate segmentation technique, the patient-specific postoperative AD model is unavailable in clinical practice, resulting in impracticable 3D morphological and hemodynamic analyses during TEVAR assessment. This work aims to construct a deep learning-based segmentation framework for postoperative type-b AD. Approach. The segmentation is performed in a two-stage manner. A multi-class segmentation of the contrast-enhanced aorta, thrombus (TH), and branch vessels (BV) is achieved in the first stage based on the cropped image patches. True lumen (TL) and false lumen (FL) are extracted from a straightened image containing the entire aorta in the second stage. A global-local fusion learning mechanism is designed to improve the segmentation of TH and BR by compensating for the missing contextual features of the cropped images in the first stage. Results. The experiments are conducted on a multi-center dataset comprising 133 patients with 306 follow-up images. Our framework achieves the state-of-the-art dice similarity coefficient (DSC) of 0.962, 0.921, 0.811, and 0.884 for TL, FL, TH, and BV, respectively. The global-local fusion learning mechanism increases the DSC of TH and BV by 2.3% (p < 0.05) and 1.4% (p < 0.05), respectively, based on the baseline. Segmenting TH in stage 1 can achieve significantly better DSC for FL (0.921 ± 0.055 versus 0.857 ± 0.220, p < 0.01) and TH (0.811 ± 0.137 versus 0.797 ± 0.146, p < 0.05) than in stage 2. Our framework supports more accurate vascular volume quantifications compared with previous segmentation model, especially for the patients with enlarged TH+FL after TEVAR, and shows good generalizability to different hospital settings. Significance. Our framework can quickly provide accurate patient-specific AD models, supporting the clinical practice of 3D morphological and hemodynamic analyses for quantitative and more comprehensive patient-specific TEVAR assessments.

A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images

Visual tracking and attribute estimation related to age or gender information of multiple person entities in a scene are mature research topics with the advent of deep learning techniques. However, when it comes to indoor images such as video sequences of retail consumers, data are not always adequate or accurate enough to essentially train effective models for consumer detection and tracking under various adverse factors. This in turn affects the quality of recognizing age or gender for those detected instances. In this work, we introduce two novel datasets: Consumers comprises 145 video sequences compliant to personal information regulations as far as facial images are concerned and BID is a set of cropped body images from each sequence that can be used for numerous computer vision tasks. We also propose an end-to-end framework which comprises CNNs as object detectors, LSTMs for motion forecasting of the tracklet association component in a sequence, along with a multi-attribute classification model for apparent demographic estimation of the detected outputs, aiming to capture useful metadata of consumer product preferences. Obtained results on tracking and age/gender prediction are promising with respect to reference systems while they indicate the proposed model’s potential for practical consumer metadata extraction.

Enhancing individual tree mortality mapping: The impact of models, data modalities, and classification taxonomy

Tree mortality is rapidly increasing as a result of more frequent and extensive droughts and forest fires across the globe. The increasing pace and scale of disturbance and resulting mortality necessitates the study of tree mortality at the scale at which it occurs (individual trees) to develop effective management strategies. Achieving this objective requires high-resolution aerial or satellite remote sensing data supported by field validation and efficient modeling. However, the optimal remote sensing data modalities and models for mapping overstory tree mortality remain uncertain and there is a lack of benchmark datasets for reproducible research, comparison, and model improvement. In this research, we propose a framework to generate tree mortality maps at the scale of individual trees and a labeled benchmark dataset using open-source high-resolution remote sensing data. Our benchmark dataset consists of over 15,000 image crops that have been manually labeled via multi-annotator majority for live, dead, mixed, and other classes in National Ecological Observatory Network (NEON) RGB, NEON hyperspectral, and National Agriculture Imagery Program (NAIP) RGBNIR imagery. Specifically, we present a multi-class classification framework that categorizes lidar-derived tree segments as live, dead, mixed, or other. We compared the performance of different machine learning models, remote sensing data modalities and their associated spatial resolution, spectral depth, and orthorectification using our benchmark dataset. Among various models, we found Convolutional Neural Network (CNN) models outperformed all other models with the highest accuracies and F1 scores across all data modalities. Similarly, among remote sensing data modalities, NEON RGB data at 0.1 m resolution performed best due to its high spatial resolution and high quality orthorectification. The overall accuracy of 87.27% with a macro F1 score of 0.76 was achieved using the RGB data with the CNN model. Our framework can be applied to any forest ecosystem in which lidar and raster imagery are available. We applied the framework using the best performing model and data modality to produce an individual tree mortality map over the Teakettle Experimental Forest in the southern Sierra Nevada. The framework and high-resolution mortality map can be valuable resources for forest managers and provide an invaluable basis for studying potential mortality drivers of dominant trees, forest inventory, selective logging, fire disturbance, and succession modeling.

AFA-Net: Adaptive Feature Attention Network in image deblurring and super-resolution for improving license plate recognition

Although a number of license plate recognition (LPR) systems have become significantly advanced, they are still far from producing ideal images. They are still fiddled with low-resolution (LR) and motion blur, which are two of the most common problems in images extracted from automobile driving environment. In order to address this issue, we present a novel LPR method that processes LR and motion blurred images from dash cams. We propose a unique framework, AFA-Net (Adaptive Feature Attention Network) organized by image pre-restoration, feature composition and image reconstruction modules. The proposed AFA-Net can generate a clear restoration image for robust LPR performance with the images obtained from dash cams in an unconstrained environment. Furthermore, we explore the novel problem, Joint-IRLPRNet (Joint-Image Restoration and License Plate Recognition Network), that simultaneously address image restoration (i.e. SR and deblurring) and LPR in an end-to end trainable manner. Moreover, we introduce a dataset called LBLP (LR and blurred license plate (LP)). The dataset is composed of 2,779 LR and motion blurred cropped LP images, extracted from unconstrained dash cams. The experimental results on LBLP dataset indicate that AFA-Net achieves 15.28% improvement in recognition accuracy, 6.47% in sequence similarity, and 3.89% in character similarity, compared to the traditional LPR model with image restoration model. Moreover, Joint-IRLPRNet can be more effective results than AFA-Net.

AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with >43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations.

A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.

Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (Fontan Outcomes Registry Using CMR Examinations [FORCE]). Materials and Methods This retrospective study used 250 cardiac MRI examinations (November 2007-December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two U-Net 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (n = 50) by using the Dice score. Volumetric and functional metrics derived from DL and ground truth manual segmentations were compared using Bland-Altman and intraclass correlation analysis. The pipeline was further qualitatively evaluated on 475 unseen examinations. Results There were acceptable limits of agreement (LOA) and minimal biases between the ground truth and DL end-diastolic volume (EDV) (bias: -0.6 mL/m2, LOA: -20.6 to 19.5 mL/m2) and end-systolic volume (ESV) (bias: -1.1 mL/m2, LOA: -18.1 to 15.9 mL/m2), with high intraclass correlation coefficients (ICCs > 0.97) and Dice scores (EDV, 0.91 and ESV, 0.86). There was moderate agreement for ventricular mass (bias: -1.9 g/m2, LOA: -17.3 to 13.5 g/m2) and an ICC of 0.94. There was also acceptable agreement for stroke volume (bias: 0.6 mL/m2, LOA: -17.2 to 18.3 mL/m2) and ejection fraction (bias: 0.6%, LOA: -12.2% to 13.4%), with high ICCs (>0.81). The pipeline achieved satisfactory segmentation in 68% of the 475 unseen examinations, while 26% needed minor adjustments, 5% needed major adjustments, and in 0.4%, the cropping model failed. Conclusion The DL pipeline can provide fast standardized segmentation for patients with single ventricle physiology across multiple centers. This pipeline can be applied to all cardiac MRI examinations in the FORCE registry. Keywords: Cardiac, Adults and Pediatrics, MR Imaging, Congenital, Volume Analysis, Segmentation, Quantification Supplemental material is available for this article. © RSNA, 2023.

An innovative smart agriculture system utilizing a deep neural network and embedded system to enhance crop yield

Wheat crop classification and prediction are important tasks for the optimization of crop yield and resource utilization. In this study, we propose an Artificial Neural Network (ANN) model integrated with Genetic Algorithm (GA) to predict and classify the wheat crop images of different ages. The dataset of 19,300 images was used, and the model was trained and tested using various performance evaluation metrics. The results show that the proposed ANN+GA model achieved the highest accuracy of 99.29% during the training phase and 98.65% during the testing phase. The model was also compared with other state-of-the-art machine learning models, and the proposed model was found to be superior in terms of accuracy, specificity, sensitivity, precision, and F-measure. The graph of training and testing accurateness and loss values in contradiction ofindividual epoch demonstrating the speediness of model convergence. Our proposed model is feasible and robust, giving better classification and crop forecast outcomes for numerous wheat crop age groups with least resource necessities. These findings could be useful for farmers and agricultural researchers in improving crop yield and resource management.

Optimal Solution for Segmentation of Malignant Melanoma Dermoscopic Images

Melanoma Malignant (MM) is the most common and dangerous form of skin cancer, which is analyzed by using Dermoscopic images in computer sciences. Segmentation technique is used to separate lesion part from healthy part in Dermoscopic images. In this research, comparison of different most popular segmented Dermoscopic image technique like Type-2 Fuzzy, Hybrid Threshold, Wavelet, Gradient Vector Flow (GVF), and Watershed etc. is approached and then better segmentation technique is proposed. In these segmentation techniques different issues like problem of hair, different color lesion, specular reflection and smoothing transaction between lesion and skin were not taken under consideration. Our methodology involves three levels of hierarchy. In the preprocessing step, it deals with problem of hair, bubble noise, smoothing and reflection noise in Dermoscopic images. These noise removals are achieved by using different filters like “Derivative of Gaussian filter and Bootomhat filter”. After region of interest is extracted then combination of threshold, image enhancement and morphological filter are used to produce the efficient algorithm for segmentation. At the end step, segmented crop image is compared with dice coefficient and experimental results of gross error rate are evaluated. For this purpose, PH² Dataset is used that contains 200 Dermoscopic images with the lesion images. The lesion images are extracted by the expert dermatologists.

Gradual OCR: An Effective OCR Approach Based on Gradual Detection of Texts

In this paper, we present a novel approach to optical character recognition that incorporates various supplementary techniques, including the gradual detection of texts and gradual filtering of inaccurately recognized texts. To minimize false negatives, we attempt to detect all text by incrementally lowering the relevant thresholds. To mitigate false positives, we implement a novel filtering method that dynamically adjusts based on the confidence levels of recognized texts and their corresponding detection thresholds. Additionally, we use straightforward yet effective strategies to enhance the optical character recognition accuracy and speed, such as upscaling, link refinement, perspective transformation, the merging of cropped images, and simple autoregression. Given our focus on Korean chart data, we compile a mix of real-world and artificial Korean chart datasets for experimentation. Our experimental results show that our approach outperforms Tesseract by approximately 7 to 15 times and EasyOCR by 3 to 5 times in accuracy, as measured using a Jaccard similarity-based error rate on our datasets.

Development of a crop monitoring system using computer vision and machine learning techniques

The growing global population demands increased agricultural production, necessitating the implementation of smart farming practices. The development of an automated crop monitoring system using computer vision and machine learning techniques can help to reduce the manual labor involved in crop management and enhance crop yield. This paper proposes a crop monitoring system that utilizes a camera mounted on a mobile robotic platform to capture images of crops at regular intervals. The images are analyzed using computer vision algorithms to detect and track plant growth, pest infestations, and nutrient deficiencies. Machine learning techniques are then applied to the data to predict crop yield. The system is designed to be scalable and can be deployed on a variety of crops, making it suitable for use in large-scale agricultural operations. Preliminary results demonstrate the system's effectiveness in detecting plant growth with an overall accuracy rate of 95%. The proposed system has the potential to significantly improve crop management practices and increase crop yield, thereby contributing to sustainable agriculture development.

Hybrid technique for crop image compression using discrete wavelet transform and sparse singular vector reconstruction

In this paper, a hybrid compression technique, based on discrete wavelet transform (DWT) and singular vector sparse reconstruction (SVSR), is proposed. In the initial phase, an original image has been divided into four sub-band by using the DWT technique. After that, SVSR is utilized for boosting the compression ratio and to improve reconstruction using piecewise cubic Hermite interpolation (PCHIP). Inverse discrete wavelet transform (IDWT) process has been applied to obtain a compressed crop image. The first time, this developed technique has been applied for compression. The performance of proposed hybrid techniques has been assessed in terms of several evaluation parameters, which are compression ratio (CR), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean square error (MSE) on ten crop image data sets. It is observed from the results that the proposed technique is quite effective in compression of different types of crop images. Further, the experimental results show that the proposed hybrid method achieved significantly higher compression over the existing techniques. The proposed technique with Haar wavelet provides 412.5% and 94.97% higher CR as compared to Singular Value Decomposition (SVD), and SVSR respectively with desirable image quality. Further, other wavelets Db4 provide 396.48% and 88.88% and Bior3.5 also provides 306.74% and 54.74% higher CR as compared to SVD, and SVSR respectively with similar image quality.

MCIP: Mining Crop Image Data On pysparkdataframe Using Feature Selection and Cluster Based Techniques

Crop-related problems such as pests and diseases in India lead to yearly losses exceeding $500 billion. Leaf blight is identified as the principal factor responsible for the substantial financial losses amounting to $500 billion. Farmers engaged in the cultivation of forage and grain sorghum experience the greatest degree of hardship. This disease has a significant impact on various crops, including maize, rice, tomato, potato, millet, and onion. The timely detection and evaluation of disease in plants can contribute to mitigating the extent of associated losses. However, the task presents difficulties as a result of variations in crop species, varieteis of crop diseases, and environmental factors. The current methodologies lack generalizability in their ability to classify and predict diseases. All of the techniques employed in this study are applied to a dataset with predetermined input values and corresponding output values. The current methodologies involve preprocessing the images and performing segmentation for extracting the appropriate characteristics. The process of segmentation necessitates the implementation of pre-processing techniques, such as dilation and edge detection. As a consequence, the loss of crucial information occurs, which subsequently leads to inaccurate classification. Furthermore, the methodologies employed thus far have not been designed to evaluate the performance of the algorithm on specialised or specific datasets. Deep learning methodologies are susceptible to the issue of overfitting. This paper proposed an approach for extracting and analysing crop image data using the PySpark (MCIP) data frame. The MCIP framework employs Principal Component Analysis (PCA) as a method for selecting pertinent features. The PCA features that have been gathered are subsequently employed to identify homogeneous subgroups through the utilisation of the K-means algorithm. The utilisation of a categorised predictive output facilitates the identification and detection of diseases present in potato leaves. The utilisation of the Multispectral Crop Imaging Platform (MCIP) extends beyond the examination of potatoes exclusively, as it possesses the capability to identify diseases present in the foliage of various agricultural crops. In order to validate our assertion, we conducted an experiment utilising the MCIP algorithm on a dataset pertaining to rice diseases. In order to assess the robustness of MCIP, we conducted an evaluation of its Accuracy, Silhouette score, speed, and F1 score. The MCIP model demonstrated high performance in terms of both speed and accuracy compared to existed approaches. The level of accuracy is remarkably near 100 percent.&#x0D; &#x0D; Index Terms— Agriculture, clustering, data mining, k-means, pca, pySpark

Weld beads and defects automatic identification, localization, and size calculation based on a lightweight fully convolutional neural network

Weld quality inspection is a key aspect of modern manufacturing. Automatic identification, location, and dimensioning of welds and defects are necessary for complex industrial environments. Weld and defects dimensional data are important indicators for high precision assessment of weld quality, while precise positioning of welds and defects can provide important location data for subsequent processing. In this study, a novel weld and defects detection, location, and size calculation method is proposed based on deep fully convolutional neural networks. Firstly, 43 high-precision high-power laser weld plate scanned images are automatically cropped to 800 pixels × 800 pixels by the proposed sliding window sampling method for training, testing, and validation, while an image data enhancement method is proposed to quadruple the amount of data to improve the model performance. Next, the OU-net image semantic segmentation model is established to accurately identify different detection targets, which can achieve MIoU and MPA values of 78.15% and 85.18% on the validation set after sufficient training, respectively. Then, the proposed cropped segmentation image fusion recovery method is used to obtain the complete weld segmentation image for a complete display of the different inspection targets on the weld plate. Finally, a detection targets localization and size calculation method is applied to accurately obtain the position data and size data of different targets. The validation results show that the proposed detection strategy can locate and measure every weld seam, collapse, overlap, spatter, and hump on the weld plate.

Deep residual neural networks with feature recalibration for crop image disease recognition

Nowadays, half of the global population depends on crop as their staple food, and crop yield is related to the food security of all mankind. crop pests and diseases are important factors affecting crop yield, and it is important to effectively prevent and efficiently detect crop pests and diseases. crop pests and diseases are diverse, previously mainly relying on manual experience to distinguish pests and disease types. With the extremely rapid development of deep learning technology, it is possible to recognise crop pests and diseases by technical methods. The diversity of pests and diseases makes higher requirements on the generalisation ability of the recognition model. In this paper, FRseNet is constructed based on ResNet-50 by introducing the SENet concept, and the experiments on the self-constructed crop pest and disease dataset show that it is capable of the task of recognising 15 kinds of diseases and 21 medium insect pests, and the performance is competitive.