Image Cropping Research Articles

Analisis Perubahan Garis Pantai Pada Wilayah Pesisir Kecamatan Amurang Kabupaten Minahasa Selatan Sulawesi Selatan

Chandra, 2023 this study is to determine the extent of coastline changes in the coastal area of Amurang District, South Minahasa Regency and the causes of coastline changes that occur in the Coastal Area of Amurang District, South Minahasa Regency. Changes in coastline are determined by the amount of sediment that goes in and out of each section of the coast. If the incoming sediment is higher than the outgoing, then the beach will experience sedimentation on the contrary, if the incoming sediment is smaller than the outgoing, then the beach will experience erosion. This research was conducted in several stages, namely collecting data and information and analyzing data. Initial image processing consists of image provisioning, image recovery, image cropping, and image sharpening. Field surveys and supporting data collection, as well as advanced image processing which includes classification, supporting data processing, image overlay and interpretation of research results. Changes in the coastline in the coastal area of Amurang District in 5 years average -1.81 Ha, abrasion with a toal of -7.23 Ha and an average accretion of 1.47 Ha with a total of 5.8 Ha, and changes in the coastline in the coastal area of Amurang district are caused by two factors, namely hydro-oceanographic factors that cause abrasion while anthrophogenic factors cause accretion.

IoT-based prediction and classification framework for smart farming using adaptive multi-scale deep networks

Agriculture is one of the prime economical sources of India and most of the people directly or indirectly depend on farming. The researchers are focusing on plant ailment detection and managing the imbalanced nutrition in plants. Automation is introduced in agricultural fields and most of these automation strategies use the Internet of Things (IoT) for enhance productivity and automate processes. With the help of several deep and machine learning approaches the endless decision-making performance is performed. Here, the endless decision performance shows appropriate outcomes which helps to solve the unstructured problems in smart farming. It is monitored that the traditional analysis doesn’t have enough decision-making ability in the selection of fertilizer quantity that is to be used in farming. This inability leads to crop ailments and that affects the lifestyle of humans too. So, the prior detection of ailments in crops is essential. Enforcing Smart Agriculture is a hot topic in research nowadays to overcome crop damage in the future. Therefore, a new IoT-based smart farming model using deep learning is proposed to increase crop yield. By detecting disease, pests, smart irrigation, and yield, the smart farming model can reduce the amount of water and chemicals used in agriculture. This smart farming model consists of four phases a) disease prediction, b) pest detection c) smart irrigation, and d) yield prediction. In the first phase, the crop images are gathered from online datasets. The diseases in crops are predicted using Multi-scale Adaptive CNN with LSTM layer (MA-CNN-LSTM), where the parameters in MA-CNN-LSTM are optimized using Advanced Mountaineering Team-Based Optimization Algorithm (AMTBO). In the second phase, the input images are given to MA-CNN-LSTM to detect crop pests. Here, the AMTBO is utilized for tuning parameters. In the third phase, the soil quality and environment data are fed into the Multi-scale Adaptive 1DCNN with LSTM layer (MA-1D CNN-LSTM) to predict the smart irrigation, where the parameter optimization is done using the AMTBO. Smart irrigation enhances the growth of crops and minimizes water usage. In the final phase, the input data such as crop quality, soil quality, and environment data are given to the MA-1D CNN-LSTM to check the overall yield prediction in an agricultural region. Here, the parameters in MA-1D CNN-LSTM are optimized via the AMTBO. The simulation results are compared with other algorithms and classification techniques to check the performance of the developed model.

Enhancing Agricultural Sustainability: Automated Crop Disease Detection through Image Processing Techniques

Abstract: The increasing demand for sustainable and high- yielding agricultural practices has driven the need for advanced technologies in crop disease detection. This paper presents a novel approach to automated crop disease detection utilizing stateof-the-art image processing techniques. In the image acquisition phase, high-resolution images of crops are captured using modern imaging devices. These images are then subjected to preprocessing techniques, including resizing, normalization, and filtering, to enhance their quality. Feature extraction follows, where relevant information is derived from the preprocessed images using advanced image processing algorithms, such as texture analysis and color-based feature extraction. The heart of the proposed system lies in the classification phase, where a machine learning or deep learning model is trained on a curated dataset comprising labeled examples of healthy and diseased crops. The integration of this automated crop disease detection system with existing agricultural frameworks provides real-time or periodic monitoring capabilities. This integration empowers farmers with timely and precise information, enabling early intervention and targeted treatment strategies to minimize crop losses. This research contributes to the ongoing efforts in sustainable agriculture by providing a reliable and efficient solution for early crop disease detection.

FarmTechBot (FTB): Empowering farmers for better harvest

In an evolving world, the FarmTechBot (FTB) emerges as a pioneering digital assistant, empowering farmers with essential insights and real-time information crucial for optimizing agricultural practices. The presence of chatbot functionality capable of addressing queries related to soil, pest, and market linkage associates to overcome hurdles for users seeking real-time information. Furthermore, the lack of voice recognition capabilities limits accessibility, especially for users who prefer or require voice-based interaction. Additionally, the manual process of pest identification from uploaded crop images hampers efficiency, prolonging the time required for pest management. The FTB represents a significant advancement in agricultural technology, addressing the existing system's limitations and empowering farmers with real-time information, enhanced accessibility, and streamlined pest management capabilities. The FTB utilizes a combination of techniques including Histogram of Oriented Gradients (HOG) for image feature extraction and classification using Support Vector Classifier (SVC). In Natural Language Processing (NLP), it employs tokenization and TF-IDF vectorization for text preprocessing and representation, alongside Linear Support Vector Classifier (LinearSVC) for intent classification.

Mango crop maturity estimation using meta‐learning approach

AbstractMangos are a significant fruit crop that is widely cultivated in tropical and subtropical regions. However, manual detection of mango crop maturity is time‐consuming and labor‐intensive. It is a vital agricultural commodity, and accurate assessment of the mango maturity stage is critical for harvesting and post‐harvest handling. Accurately detecting mango crop maturity is essential for ensuring optimal harvesting and quality. This study introduced a specialized Meta‐learning method for classifying mango crop images with limited data. The conventional approach to maturity classification, often hindered by limited labeled data, is significantly enhanced by meta‐learning's ability to adapt quickly to new tasks with minimal examples. The proposed approach works as initial image segmentation to isolate mango crop regions, comprehensive feature extraction to capture meaningful data representations, and a critical meta‐training. This phase involves refining a classifier within a metric space by utilizing cosine distance and an adaptable scale parameter, this is followed by a meta‐testing stage. where the adapted classifier is utilized for maturity prediction with minimal support samples. This research holds substantial promise for the agricultural sector, offering practical solutions to optimize harvest management practices and improve mango crop yield predictions.Practical applicationsOptimized Harvest Scheduling: Scenario: Farmers can use crop maturity estimation to schedule harvest times more precisely. Application: Knowing crop maturity optimizes harvest, minimizing losses. Resource Management: Scenario: Efficient use of resources such as water, fertilizers, and pesticides is essential for sustainable agriculture. Application: Crop maturity guides resource use, for example, reducing late‐stage water/nutrients prevents overuse, saves resources. Quality Assurance: Scenario: Crop quality is often linked to its maturity level. Application: Maturity estimation assesses crop quality, guiding post‐harvest planning and determining market value. Data‐Driven Decision Making: Scenario: Modern agriculture relies on data for decision‐making. Application: Maturity estimation promotes data‐driven farming, aiding trend analysis, optimizing planting, and enhancing farm management.

Segmentation of void defects in X-ray images of chip solder joints based on PCB-DeepLabV3 algorithm

Defects within chip solder joints are usually inspected visually for defects using X-ray imaging to obtain images. The phenomenon of voids inside solder joints is one of the most likely types of defects in the soldering process, and accurate detection of voids becomes difficult due to their irregular shapes, varying sizes, and defocused edges. To address this problem, an X-ray void image segmentation algorithm based on improved PCB-DeepLabV3 is proposed. Firstly, to meet the demand for lightweight and easy deployment in industrial scenarios, mobilenetv2 is used as the feature extraction backbone network of the PCB-DeepLabV3 model; then, Attentional multi-scale two-space pyramid pooling network (AMTPNet) is designed to optimize the shallow feature edges and to improve the ability to capture detailed information; finally, image cropping and cleaning methods are designed to enhance the training dataset, and the improved PCB-DeepLabV3 is applied to the training dataset. The improved PCB-DeepLabV3 model is used to segment the void regions within the solder joints and compared with the classical semantic segmentation models such as Unet, SegNet, PSPNet, and DeeplabV3. The proposed new method enables the solder joint void inspection to get rid of the traditional way of visual inspection, realize intelligent upgrading, and effectively improve the problem of difficult segmentation of the target virtual edges, to obtain the inspection results with higher accuracy.

A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation

With the widespread adoption of smart farms and continuous advancements in IoT (Internet of Things) technology, acquiring diverse additional data has become increasingly convenient. Consequently, studies relevant to deep learning models that leverage multimodal data for crop disease diagnosis and associated data augmentation methods are significantly growing. We propose a comprehensive deep learning model that predicts crop type, detects disease presence, and assesses disease severity at the same time. We utilize multimodal data comprising crop images and environmental variables such as temperature, humidity, and dew points. We confirmed that the results of diagnosing crop diseases using multimodal data improved 2.58%p performance compared to using crop images only. We also propose a multimodal-based mixup augmentation method capable of utilizing both image and environmental data. In this study, multimodal data refer to data from multiple sources, and multimodal mixup is a data augmentation technique that combines multimodal data for training. This expands the conventional mixup technique that was originally applied solely to image data. Our multimodal mixup augmentation method showcases a performance improvement of 1.33%p compared to the original mixup method.

A multi-vision monitoring framework for simultaneous real-time unmanned aerial monitoring of farmer activity and crop health

Current remote sensing technologies employing Unmanned Aerial Vehicles (UAVs) for farm monitoring have shown promise in characterizing the environment through diverse sensor systems, including hyperspectral cameras, LiDAR, thermal cameras, and RGB sensors. However, these solutions often specialize in either activity recognition or crop monitoring, but not both. To address this limitation and enhance efficacy, we propose a multi-vision monitoring (MVM) framework capable of simultaneously recognizing farm activities and assessing crop health. Our approach involves computer vision techniques that transform aerial videos into sequential images to extract essential environmental features. Central to our framework are two pivotal components: the Farmer Activity Recognition (FAR) algorithm and the Crop Image Analysis (CIA). The FAR algorithm introduces a novel feature extraction method capturing motion across various maps, enabling distinct feature sets for each activity. Meanwhile, the CIA component utilizes the normalized Triangular Greenness Index (nTGI) to estimate leave chlorophyll levels, an important indicator for crop health. By unifying these components, we achieve dual functionality—activity recognition and crop health estimation—using identical input data, thereby enhancing efficiency and versatility in farm monitoring. Our framework employs a diverse range of machine learning models, demonstrating the potential of our extracted features to address the defined problem effectively in unison.

Breast Cancer Detection in Mammography using Faster Region Convolutional Neural Networks and Group Convolution

Breast cancer is a prevalent and potentially life-threatening medical condition characterized by uncontrolled cell proliferation within breast tissue. This global health concern predominantly among women, although it can affect men as well. The timely and accurate detection of breast cancer is critical, as it significantly influenced treatment outcomes and survival rates. Despite advancements in human-based diagnostic methods, inherent limitations such as subjectivity, human error, and fatigue persist. To address these constraints, computer-vision-based techniques have been explored for breast cancer detection, aiming to enhance early detection, reduce diagnostic errors, and improve patient outcomes. This study advances previous approaches by integrating group convolution and the Special Euclidean motion group as a features extractor into the Faster Region Convolutional Neural Networks framework of Detectron2. This integration offers the advantage of enhancing the Convolutional Neural Network by incorporating a method to preserve invariant and equivariant features, effectively leveraging symmetries in mammography images. The INbreast dataset, comprising 410 images from 115 cases, including 90 instances with bilateral breast involvement in women, was utilized. To ensure data compatibility, DICOM to PNG and XML to JSON format conversions were performed. A data enhancement pipeline, encompassing techniques such as image cropping, truncation normalization, contrast-limited adaptive histogram equalization, and image synthesis, was employed for data preprocessing and cleaning. The proposed technique demonstrated competitive results in breast cancer detection, achieving a recall rate of 97.22. This underscores the efficacy of the integrated approach in improving diagnostic accuracy and holds promise for advancing computer-vision-based breast cancer detection methodologies.

Research on wheat image encryption based on different chaotic systems

To study the influence of chaotic systems on wheat image encryption, a method to determine the optimal chaotic system encryption based on a wheat image is proposed. Ten different chaotic system schemes were combined to encrypt the wheat images using 13 common chaotic maps. The best chaotic system scheme was obtained by considering the anti-attack capability of these encryption schemes, which analyzed eight commonly used image encryption performance evaluation indexes. The experimental results show that the new four-dimensional chaotic system has the best encryption effect and is suitable for wheat image encryption. The proposed scheme for wheat image encryption based on chaotic systems provides a reference for other crop image encryption methods.

205 Detection of anemic sheep using ocular conjunctiva images and deep learning technology

Abstract In sheep, severe anemia often results from gastrointestinal nematodes infections, commonly caused by Haemonchus contortus, a blood-sucking nematode. The objective of the study was to develop a model to predict packed cell volume (PCV) in sheep through ocular conjunctiva images as a real-time anemia diagnosis approach. We collected 3,291 ocular conjunctiva images from 392 sheep on three different farms using an iPhone 8 (12-megapixel). To identify the region of interest in the images (ocular conjunctiva), we annotated 480 images using the Segment Anything Model (SAM). Subsequently, we employed an image segmentation algorithm based on U-net, utilizing the original images and annotations obtained from SAM. We then cropped the segmented images to retain only the region containing the ocular conjunctiva. These cropped and segmented images were used as input data, with PCV as the target variable, in both classification and regression models, while exploring three different deep neural network (DNN) architectures: VGG19, Inception v3, and Xception. We set a threshold of 27% (anemic < 27%, non-anemic ≥ 27%) to convert PCV into a binary variable for classification models. The dataset was split in train, validation and test sets using random by sheep strategy to perform the analysis of the segmentation and prediction models. The segmentation was tested using intersection over union (IoU) as an evaluation metric. To assess and compare predictive quality in the test set, we calculated accuracy, precision, recall, and F1 score for classification models, and R2 for regression models. The U-net segmentation model demonstrated good localization accuracy and reliable segmentation ability, with an average IoU of 0.93, 0.84 and 0.68 in the train, validation and test set, respectively. VGG19 outperformed the other models in classifying individuals as anemic or non-anemic, achieving an F1 score of 0.60, indicating its moderate ability to distinguish between these two classes. For regression, Xception provided the best performance with an R2 of 0.29, suggesting that 29% of the variance in PCV can be explained by the model’s predictions. This innovative approach not only expands the possibilities for integrated high-throughput phenotyping through computer vision but also aids in identifying anemic animals. The results suggest that using ocular conjunctiva images and DNN technology can contribute to support farm-level management decisions, and potentially reduce economic losses due to parasitic infections like H. contortus. Furthermore, this approach reduces the cost of blood tests for anemia identification while allowing for real-time anemia diagnosis.

OHSCR: Benchmarks Dataset for Offline Handwritten Sindhi Character Recognition

This research work presents a unique dataset for offline handwritten Sindhi character recognition. It has 7800 character images in total, divided into multiple categories by 150 writers of various ages, genders, and professional backgrounds. Each writer writes the 52 Sindhi characters in the designed form. With a high-quality scanner, all of the written samples were scanned. After that, all the handwritten Sindhi characters were cropped from the collected designed form, and the cropped images were saved in ‘.png’ format. For the benefit of the Sindhi research community, this work suggests an image dataset for character recognition in handwritten Sindhi. The dataset will be made publically available. For the Sindhi language, this dataset can be used to create and test handwritten character recognition systems and provide helpful insights through writer identification. The dataset has been divided into the training set and the test set, with 80% for training and 20% for testing. The different preprocessing techniques used to remove noise from scanned images to create a clean dataset. The dataset created as a result of this research is the world's first openly accessible dataset for handwritten research, and it can be useful for writer identification systems and handwriting recognition systems.

A Comparison of CNN-based Image Feature Extractors for Weld Defects Classification

<span lang="EN-US">Classification of the types of weld defects is one of the stages of evaluating radiographic images, which is an essential step in controlling the quality of welded joints in materials. By automating the weld defects classification based on deep learning and the CNN architecture, it is possible to overcome the limitations of visually or manually evaluating radiographic images. Good accuracy in classification models for weld defects requires the availability of sufficient datasets. In reality, however, the radiographic image dataset accessible to the public is limited and imbalanced between classes. Consequently, simple image cropping and augmentation techniques are implemented during the data preparation stage. To construct a weld defect classification model, we proposed to utilize the transfer learning method by employing a pre-trained CNN architecture as a feature extractor, including DenseNet201, InceptionV3, MobileNetV2, NASNetMobile, ResNet50V2, VGG16, VGG19, and Xception, which are linked to a simple classification model based on multilayer perceptron. The test results indicate that the three best classification models were obtained by using the DenseNet201 feature extractor with a test accuracy value of 100%, followed by ResNet50V2 and InceptionV3 with an accuracy of 99.17%. These outcomes are better compared to state-of-the-art classification models with a maximum of six classes of defects. The research findings may assist radiography experts in evaluating radiographic images more accurately and efficiently.</span>

CSIU-Net+: Pepper and corn leaves classification and severity identification using hybrid optimization

An agricultural product plays a major role in the economical growth of developing countries. Agricultural products like pepper and corn are the essential crops with respect to human health food security. But, these two crops are prone to different diseases such as gray leaf spot, common rust and fruit rot which affects the productivity of crops. Further, the severity identification is also a challenging one. To address these limitations, this work presents different approaches for identifying the crop lesions and predicting the severity and thereby increasing the productivity of crops. The development of the proposed model includes steps such as dataset collection, noise removal, segmentation, feature extraction, classification and severity prediction. Initially, the crop images are pre-processed by the median filter and the pre-processed images are processed are segmented, extracted and classified by the optimized U-Net model. Moreover, hybrid optimizer which is the integration of GJA (Golden jackal algorithm) and RDA (Red deer algorithm) are utilized for precise segmentation and classification. Finally, the severity prediction is computed for the diseased leaves by the measuring the size of image pixels. The experimentation is carried out on the benchmark PlantVillage dataset; the accuracy and precision values achieved are 99.2% and 99.1%. Thus, the experimental outcomes show the effectiveness and stability of the model.

Multi-Scale Contextual Swin Transformer for Crop Image Segmentation

Combining UAV-based remote sensing with deep learning for image segmentation is a particularly innovative and effective technology in modern agriculture. This approach allows for detailed and precise analysis of agricultural fields, such as crop monitoring, yield prediction, and irrigation management, enhancing decision-making and farm management practices. Inspired by the recent advancements of Transformers in computer vision, this paper introduces the Multi-Scale Contextual Swin Transformer (MSC-Swin), a novel model for precise segmentation of UAV crop images. MSC-Swin innovatively combines a Swin Transformer architecture for detailed feature extraction with pooling operations to utilize multi-scale contextual information. Our extensive experimentation demonstrates that MSC-Swin not only achieves state-of-the-art performance on the Barley Remote Sensing dataset, with a record mIoU of 86.4% on the test set, but also exhibits robustness and excellent generalizability.

Learning Vehicle Dynamics From Cropped Image Patches for Robot Navigation in Unpaved Outdoor Terrains

Learning Vehicle Dynamics From Cropped Image Patches for Robot Navigation in Unpaved Outdoor Terrains

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2).

A Review on Crop and Weed Identification Using Deep Learning

Agriculture remains the backbone of several economies in the world, especially in underdeveloped countries. With the rapid growth of the population and the increasing demand for food, farmers need to maximize productivity and one possibility is the reduction of losses. Weeds are one of the major dangers in farming. Indeed, they compete vigorously with the crop for nutrients and water. Improved methods are required to get good yields from crops. The proposed model aims to organize a diverse dataset of crop and weed images, leveraging Convolutional Neural Networks (CNN) for crop and weed identification, texture feature extraction, and employing CNN for precise crop and weed identification. The paper introduces a novel deep-learning approach utilizing Residual Neural Networks (ResNet) for effectively identifying and classifying crops and weeds in agriculture. The findings underscore the effectiveness of various deep learning models such as CNNs, in accurately detecting weeds within crops, aided by preprocessing techniques and model optimization. These advancements hold promising prospects for revolutionizing agricultural practices and enhancing productivity in the future. Key Words: Agriculture, Weed Management, Crop Identification, Convolutional Neural Networks, Residual Neural Networks, Deep Learning

Hyperspectral crop image classification via ensemble of classification model with optimal training

Agriculture is a significant source of income, and categorizing the crop has turned into vital factor that aids more in the crop production sector. Traditionally, crop development stage determination is done manually by eye inspection. However, producing high-quality crop type maps using modern approaches remains difficult. In this paper, the hyperspectral crop image classification model is proposed that includes four stages, they are (a) preprocessing, (b) segmentation, (c) feature extraction and (d) classification. In the preprocessing step, the hyperspectral image is provided as input, where the filtering process will carried out using median filtering. The filtered image is then used as the segmentation’s input. The image is segmented in the segmentation step using the enhanced entropy-based fuzzy c-means technique. Subsequently, spectral spatial features and vegetation index-based features are derived from segmented images. The final step is the classification, where the ensemble of classification model will be used that includes models like Convolutional Neural Networks (CNN), Deep Maxout (DMO), Recurrent Neural Networks (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU), respectively. The proposed Self Improved Tasmanian devil Optimization (SI-TDO) approach has optimally adjusted the Bi-GRU model’s training weights to enhance ensemble classification performance. Finally, the effectiveness of the proposed SI-TDO method compared to the traditional algorithm is examined for several metrics. The SI-TDO obtained the greatest accuracy of 94.68% in training rate 80, while other existing models have the lowest ratings.

SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

Crop disease detection and crop baking stage judgement require large image data to improve accuracy. However, the existing crop disease image datasets have high asymmetry, and the poor baking environment leads to image acquisition difficulties and colour distortion. Therefore, we explore the potential of the self-attention mechanism on crop image datasets and propose an innovative crop image data-enhancement method for recurrent generative adversarial networks (GANs) fused with the self-attention mechanism to significantly enhance the perception and information capture capabilities of recurrent GANs. By introducing the self-attention mechanism module, the cycle-consistent GAN (CycleGAN) is more adept at capturing the internal correlations and dependencies of image data, thus more effectively capturing the critical information among image data. Furthermore, we propose a new enhanced loss function for crop image data to optimise the model performance and meet specific task requirements. We further investigate crop image data enhancement in different contexts to validate the performance and stability of the model. The experimental results show that, the peak signal-to-noise ratio of the SM-CycleGAN for tobacco images and tea leaf disease images are improved by 2.13% and 3.55%, and the structural similarity index measure is improved by 1.16% and 2.48% compared to CycleGAN, respectively.