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Artykuły w czasopismach na temat „Chili leaf image”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Suwarningsih, Wiwin, Rinda Kirana, Purnomo Husnul Khotimah, et al. "Chili leaf segmentation using meta-learning for improved model accuracy." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2209–21. https://doi.org/10.11591/eei.v14i3.7929.

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Recognizing chili plant varieties through chili leaf image samples automatically at low costs represents an intriguing area of study. While maintaining and protecting the quality of chili plants is a priority, classifying leaf images captured randomly requires considerable effort. The quality of the captured leaf images significantly impacts the development of the model. This study applies a meta-learning approach to chili leaf image data, creating a dataset and classifying leaf images captured using mobile devices with varying camera specifications. The images were organized into 14 experimen

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Siddiqui, Faisal Mubeen. "Chili Leaf Disease Prediction Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4791–97. http://dx.doi.org/10.22214/ijraset.2023.52757.

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Abstract: Chili leaf diseases cause significant damage to chili plants, leading to reduced crop yield and economic losses for farmers. Early detection and diagnosis of these diseases are crucial for effective disease management. In this research paper, we propose a chili leaf disease prediction model using Convolutional Neural Network (CNN). The proposed model utilizes an image dataset collected from different regions ,consisting of chili leaf images infected with common chili leaf diseases, like bacterial leaf spot, leaf Curl , Mosaic virus, etc. We pre-processed the dataset to enhance the im

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Irene Oktaviani Duka, Huan Arthur Ado, and Yampi R.Kaesmetan. "Identifikasi Penyakit Tanaman Citra Daun Cabe Menggunakan Gray Level Co-Occurrencce Matrix Dan Support Vector Machine." Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi 2, no. 2 (2024): 41–52. http://dx.doi.org/10.61132/neptunus.v2i2.86.

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Disease control of chili leaf citra plants is an important aspect in modern agriculture to increase crop yields and reduce losses due to pest attacks on chili leaf citra plants. In this research, identification of chili leaf diseases uses Gray Level Co-Occurrence to obtain image features, and the Support Vector Machine (SVM) method is used to classify the feature extraction results according to leaf disease categories in the test image. Based on the disease class using the test image. .As a classification tool for identifying plant pests in images of chili leaves, the dataset used in this rese

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Wulandari, Yestika Dian, Lulu Chaerani Munggaran, Foni Agus Setiawan, and Ika Atman Satya. "Chili Leaf Health Classification using Xception Pretrained Model." SISTEMASI 13, no. 3 (2024): 1084. http://dx.doi.org/10.32520/stmsi.v13i3.3943.

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As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are e

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Dumaria, Triyani, Sri Hendrastuti Hidayat, and Purnama Hidayat. "Metode Termografi Inframerah untuk Deteksi Dini Pepper yellow leaf curl virus pada Tanaman Cabai." Jurnal Fitopatologi Indonesia 19, no. 1 (2023): 1–10. http://dx.doi.org/10.14692/jfi.19.1.1-10.

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Infrared Thermography for Early Detection of Pepper yellow leaf curl virus on Chili Plants Observations of plant pests and diseases are generally carried out by looking for visual symptoms for each disease target. Agricultural technology 4.0 began to be used for the development of plant disease detection methods. It was reported that there were differences in color and temperature between diseased and healthy plants which could be recorded by a thermal camera. This study aimed to determine the potential of the FLIR One Pro-IOS thermal camera to record differences in color and temperature betwe

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Pertiwi, S., H. P. Ipung, and B. P. W. Sukarno. "Prototype of chili pathogen early detection system by using multispectral NIR/NUV." IOP Conference Series: Earth and Environmental Science 1386, no. 1 (2024): 012032. http://dx.doi.org/10.1088/1755-1315/1386/1/012032.

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Abstract Chili plants (Capsicum annuum L.) are a high-value horticultural commodity but are very susceptible to disease. Therefore, early detection of chili disease is essential to minimize the potential loss in chili farming. This research aims to develop a prototype for early detection of chili diseases before they become apparent to the human eye. In response to pathogens, chili plants produce substances that actively absorb and reflect ultraviolet light, while near-infrared images can reveal leaf cell structure damage. By considering these plant defense systems, the prototype system, devel

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Achmad Naila Muna Ramadhani, Galuh Wilujeng Saraswati, Rama Tri Agung, and Heru Agus Santoso. "Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 4 (2023): 940–46. http://dx.doi.org/10.29207/resti.v7i4.5028.

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Chili is an important agricultural commodity in Indonesia and plays a significant role in the nation's economic growth. Its demand by households and industries reaches up to 61%. However, this high demand also means that monitoring efforts need to be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not promptly addressed, they can lead to a decrease in production levels, which can negatively affect the economy. With technological advancements, automatic monitoring using image processing is now highly feasible, making monitoring more effic

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Achmad Naila Muna Ramadhani, Galuh Wilujeng Saraswati, Rama Tri Agung, and Heru Agus Santoso. "Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 4 (2023): 940–46. http://dx.doi.org/10.29207/resti.v7i4.5028.

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Chili is an important agricultural commodity in Indonesia and plays a significant role in the nation's economic growth. Its demand by households and industries reaches up to 61%. However, this high demand also means that monitoring efforts need to be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not promptly addressed, they can lead to a decrease in production levels, which can negatively affect the economy. With technological advancements, automatic monitoring using image processing is now highly feasible, making monitoring more effic

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Abdul Manan, Amirul Asyraf, Mohd Azraai Mohd Razman, Ismail Mohd Khairuddin, and Muhammad Nur Aiman Shapiee. "Chili Plant Classification using Transfer Learning models through Object Detection." MEKATRONIKA 2, no. 2 (2020): 23–27. http://dx.doi.org/10.15282/mekatronika.v2i2.6743.

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This study presents an application of using a Convolutional Neural Network (CNN) based detector to detect chili and its leaves in the chili plant image. Detecting chili on its plant is essential for the development of robotic vision and monitoring. Thus, helps us supervise the plant growth, furthermore, analyses their productivity and quality. This paper aims to develop a system that can monitor and identify bird’s eye chili plants by implementing machine learning. First, the development of methodology for efficient detection of bird’s eye chili and its leaf was made. A dataset of a total of 1

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Suwarningsih, Wiwin, Purnomo Husnul Khotimah, Andri Fachrur Rozie, et al. "Ide-cabe: chili varieties identification and classification system based leaf." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 445–53. http://dx.doi.org/10.11591/eei.v11i1.3276.

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Identifying good quality chili varieties can be done by observing their leaves. It is required for seed testing and certification processes. Currently, a manual leaf identification method is used in which human experts inspect a wide range of leaves every one to two months. An automatic method could increase the identification process. Deep learning has proven to be a prominent method for image classification. We investigate the performance of deep CNN models, as: AlexNet, VGG16, Inception-v3 and DenseNet-121; to classify chili variety. In this paper, we took images of leaves aged 10 days. A p

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Wiwin, Suwarningsih, Husnul Khotimah Purnomo, Fachrur Rozie Andri, et al. "Ide-cabe: chili varieties identification and classification system based leaf." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 445–53. https://doi.org/10.11591/eei.v11i1.3276.

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Identifying good quality chili varieties can be done by observing their leaves. It is required for seed testing and certification processes. Currently, a manual leaf identification method is used in which human experts inspect a wide range of leaves every one to two months. An automatic method could increase the identification process. Deep learning has proven to be a prominent method for image classification. We investigate the performance of deep CNN models, as: AlexNet, VGG16, Inception-v3 and DenseNet-121; to classify chili variety. In this paper, we took images of leaves aged 10 days. A p

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., Basiroh, Nuning Kurniasih, Dian Asmara Jati, Nina Zulida Situmorang, Heni Sukrisno, and Sujito . "Analysis of Leaf Features in Chili Plants Using Automated Color Equalization (ACE)." International Journal of Engineering & Technology 7, no. 2.13 (2018): 457. http://dx.doi.org/10.14419/ijet.v7i2.13.18139.

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Chili is a variety of crop groups that have promising business prospects. To obtain optimal agricultural yield, then the process of plant care and how to planting should be maximal. Constraints often experienced by farmers in the process of planting chili in Magelang regency of Indonesia is a disease of yellow leaves. Some diseases in plants can be identified using precision technology, one of them is by using an image or image-based technology. In previous studies, no one has analyzed using feature extraction using ACE as an analysis to detect plant disease in chili. In this study will extrac

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Chauhan Pareshbhai Mansangbhai. "Chili Disease Detection Using HOG with Euclidean Distance." Journal of Electrical Systems 20, no. 3 (2024): 1577–84. http://dx.doi.org/10.52783/jes.3654.

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In order to detect plant diseases in the leaves of chili plants, automatic learning is used in this study. Farmers are planting chilies with the intention of exporting them worldwide. Chili is a need for regular meals. There aren't many illnesses that need to be found in the leaves of chili plants. There are three types of chili plants: weak, diseased, and healthy. Weak and sick chili plants can be affected by diseases such as a harsh leaf, spot leaf, whitefly, yellowish, etc. It has been reported that research is underway to determine whether chile plants are safe to grow or polluted. But whe

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Muslim, Rudi, Zaeniah Zaeniah, Ardiyallah Akbar, Bahtiar Imran, and Zaenudin Zaenudin. "Disease Detection of Rice and Chili Based on Image Classification Using Convolutional Neural Network Android-Based." Jurnal Pilar Nusa Mandiri 19, no. 2 (2023): 85–96. http://dx.doi.org/10.33480/pilar.v19i2.4669.

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The current development of machine learning makes it easier for humans to obtain information, especially from images. The presence of processing assistance from machines can increase the accuracy of the information provided to further convince the recipient of the information. Rice and chili farmers in Indonesia have experienced many disease attacks from several types of plant diseases. Not many farmers understand and are good at guessing the diseases that attack their rice and chili plants. So many rice and chili farmers experienced crop failure. This research aims to build a disease-detectio

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Taufik, M., M. Z. Firihu, A. Hasan, V. I. Variani, H. S. Gusnawaty, and M. Botek. "Vegetation index value on chili leaves with symptoms of geminivirus disease (case study in Konda district, Konawe Selatan regency, Southeast Sulawesi)." IOP Conference Series: Earth and Environmental Science 1182, no. 1 (2023): 012004. http://dx.doi.org/10.1088/1755-1315/1182/1/012004.

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Abstract Expression of symptoms on chili plants infected with the Geminivirus can vary. This variation can be determined by viral satellite DNA (alphasatellites, betasatellites, deltasatelites) and the degree of plant resistance. The visual limitations of the human eye become an obstacle in recognizing and assessing the severity of plant virus symptoms in the field. It is hoped that an assessment method based on the normalized difference vegetation index (NDVI) value obtained from the processing of symptomatic plant images is expected to be a solution. No precise information has been found reg

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Vasavi, Pallepati, A. Punitha, and T. VenkatNarayana Rao. "Chili Leaf Disease Detection Using Deep Feature Extraction." Journal of Intelligent Systems and Internet of Things 9, no. 2 (2023): 222–30. http://dx.doi.org/10.54216/jisiot.090216.

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Please Diseases in crops lead to decreased production, which can be addressed through consistent surveillance. Manual surveillance of crop diseases is both arduous and prone to mistakes. The timely identification of crop leaf diseases using Computer Vision and Artificial Intelligence can aid in minimizing the negative impact of diseases and address the limitations of continuous human surveillance. To classify chili crop diseases, this research paper introduces a new deep feature extraction model based on Transfer Learning using ResNet50, MobileNet, EfficientNetB0, and multiple classifiers. On

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Pallepati Vasavi, Et al. "Image based Chili Crop Disease Prediction Using Deep Transfer Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1145–49. http://dx.doi.org/10.17762/ijritcc.v11i10.8635.

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Crop diseases have a terrible impact on food protection and can result in considerable reductions in both the supply and quality of agricultural products. Human professional have traditionally been relying on to diagnose crop diseases caused by insects, pests, virus, bacteria, fungal, inadequate nutrition, or adverse environmental conditions. This, however, is costly, time demanding, and in some situations unworkable. Thus, in the area of agricultural information, the automatic identification of crop diseases is significantly required. Many strategies have been presented to solve this challeng

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Suwarningsih, Wiwin, Rinda Kirana, Purnomo H Khotimah, et al. "Emphasizing Data Quality for the Identification of Chili Varieties in the Context of Smart Agriculture." Interdisciplinary Journal of Information, Knowledge, and Management 19 (2024): 005. http://dx.doi.org/10.28945/5257.

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Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addr

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Muthia, Fathi, and Nur Sultan Salahuddin. "Development of a Robotic System for Agricultural Pest Detection: A Case Study on Chili Plants." Advance Sustainable Science Engineering and Technology 7, no. 1 (2025): 02501019. https://doi.org/10.26877/asset.v7i1.1152.

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Chili peppers, a key agricultural commodity in Indonesia, are highly susceptible to pest infestations and diseases, leading to significant economic losses and challenges in sustainable farming. This study presents the design and implementation of a real-time pest detection system that integrates robotics, computer vision, and deep learning to enhance agricultural productivity. The system is built on a Raspberry Pi 5 and Arduino Mega Pro Mini, utilizing a camera for image capture and ultrasonic sensors for navigation. A ResNet-based model was trained on a dataset of 2,703 chili leaf images, cat

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Basiroh, Basiroh. "Desease Identification In Plant Leaf Image of Chili (Capsicum Annum (L)) Using Image Processing and Automated Colour Equalization (ACE) Algorithm." Indonesian Journal of Artificial Intelligence and Data Mining 1, no. 2 (2018): 99. http://dx.doi.org/10.24014/ijaidm.v1i2.5644.

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The world of agriculture becomes one of the vital objects and one of the promising business prospects. To obtain optimal agricultural yield, the process of plant care and the way of planting should be really - maximal, because the main key in seeking maximum results in terms of quality and quantity. Harvest failures are the least desirable to farmers and crop failures are the number one scariest specter for cultivating farmers. Today's informatics technology has been developed in an effort to support increased yields in the agricultural sector. This study measured the level of accuracy of resu

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Leite, Douglas Vieira, Alisson Vasconcelos de Brito, Gregorio Guirada Faccioli, and Gustavo Haddad Souza Vieira. "Deep Learning Models for Detection and Severity Assessment of Cercospora Leaf Spot (Cercospora capsici) in Chili Peppers Under Natural Conditions." Plants 14, no. 13 (2025): 2011. https://doi.org/10.3390/plants14132011.

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The accurate assessment of plant disease severity is crucial for effective crop management. Deep learning, especially via CNNs, is widely used for image segmentation in plant lesion detection, but accurately assessing disease severity across varied environmental conditions remains challenging. This study evaluates eight deep learning models for detecting and quantifying Cercospora leaf spot (Cercospora capsici) severity in chili peppers under natural field conditions. A custom dataset of 1645 chili pepper leaf images, collected from a Brazilian plantation and annotated with 6282 lesions, was d

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Taufik, Muhammad, Muhammad Zamrun Firihu, Asmar Hasan, et al. "Begomoviruses on two chili types in Southeast Sulawesi Indonesia: variation of symptom severity assessment and DNA-betasatellite identification." Jurnal Hama dan Penyakit Tumbuhan Tropika 24, no. 1 (2023): 1–9. http://dx.doi.org/10.23960/jhptt.1241-9.

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The association of viral satellite DNA with Begomoviruses influences symptom expression in infected plants. Normalized Difference Vegetation Index (NDVI) is an image processing method used to assess plant health based on the plant’s ability to absorb sunlight for photosynthesis. Therefore, this study aims to assess symptom severity based on symptom variation and NDVI, as well as to detect and identify the presence of beta-satellite DNA associated with chili plants. The study was conducted in North Kolaka Regency, Southeast Sulawesi Province, Indonesia. It involved observations and image record

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Syaban, Kharis, and Agus Harjoko. "Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 2 (2016): 161. http://dx.doi.org/10.22146/ijccs.16628.

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Compared with other methods of classifiers such as cellular and molecular biological methods, using the image of the leaves become the first choice in the classification of plants. The leaves can be characterized by shape, color, and texture; The leaves can have a color that varies depending on the season and geographical location. In addition, the same plant species also can have different leaf shapes. In this study, the morphological features of leaves used to identify varieties of pepper plants. The method used to perform feature extraction is a moment invariant and basic geometric features

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Yetilmezsoy, Kaan, Fatih Ilhan, and Emel Kıyan. "Utilization of Non-Composted Human Hair Hydrolysate as a Natural and Nutrient-Rich Liquid Fertilizer for Sustainable Agro-Applications and Bio-Waste Management." Sustainability 17, no. 4 (2025): 1641. https://doi.org/10.3390/su17041641.

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Human hair, commonly considered a discarded organic waste, is a keratin-rich material with remarkable potential for sustainable agriculture as an innovative resource. This study systematically explored the potential of non-composted human hair hydrolysates as eco-friendly and nutrient-rich liquid fertilizers, emphasizing their ability to enhance agricultural sustainability and mitigate organic waste accumulation. Eight distinct hydrolysates prepared with alkaline solutions were evaluated for their effects on plant growth using red-hot chili pepper (Capsicum frutescens) as the primary model und

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Sai, Varkala Tarun, Nallam Eswara Sai Akhil, Tandra Jaya Mallika Jashnavi, and Naga Venkata Kashim Kanakala. "Image Quality Enhancement for Wheat rust Diseased Leaf Image using Histogram Equalization & CLAHE." E3S Web of Conferences 391 (2023): 01029. http://dx.doi.org/10.1051/e3sconf/202339101029.

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In the domain of agriculture, few crops play an important role as wheat is one of them. It is one of the most important one’s across the globe. Nearly providing 15% food production across the world, it is also a winter cereal crop and a most essential food. The real challenge is to enhance the images of wheat crop in the agricultural area. because some of these are captured in real space environments may not be that clear to predict the type of disease of the crop that it is suffering from. So, we enhance the captured images using few existing techniques using the image histograms and the furt

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Aminuddin, Nuramin Fitri, Herdawatie Abdul Kadir, Mohd Razali Md Tomari, Ariffuddin Joret, and Zarina Tukiran. "TOWARDS IMPROVED DISEASE IDENTIFICATION WITH PRETRAINED CONVOLUTIONAL NEURAL NETWORKS AS FEATURE EXTRACTORS FOR CHILI LEAF IMAGES." Jurnal Teknologi 86, no. 2 (2024): 89–100. http://dx.doi.org/10.11113/jurnalteknologi.v86.19853.

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Chili is a popular crop that is widely grown due to its flavorful and spicy fruit that is nutritionally beneficial. For the benefit of economic growth, it is important to precisely assess the chili health. With the advancement of computer vision-based applications, methods such as feature descriptors have been utilized to assist farm owners in identifying chili diseases via chili leaf images. However, these feature descriptors still require the manual extraction of disease features in order to accurately identify chili diseases. In this research, pretrained Convolutional Neural Networks (CNNs)

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Keerthi, M. "Disease Detection and Remote Monitoring in Chilli Crop Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2988–95. http://dx.doi.org/10.22214/ijraset.2021.37843.

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Abstract: Observations today have verified that the average crop yield in India is declining due to illnesses that have affected fully grown plants. Chilli plant production is tough due to the plant's vulnerability to a variety of microorganisms, infectious illnesses, and pests. Infections in the chilli plant impact areas such as the leaves and stems. In the early stages of diagnosing chilli illnesses, leaf characteristics are examined. The leaf image is taken and analyzed to determine the health of the chilli plant. Pesticides are currently being tested on chilli plants on a regular basis wit

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Anwar, Masrur, Yosi Kristian, and Endang Setyati. "Klasifikasi Penyakit Tanaman Cabai Rawit Dilengkapi Dengan Segmentasi Citra Daun dan Buah Menggunakan Yolo v7." INTECOMS: Journal of Information Technology and Computer Science 6, no. 1 (2023): 540–48. http://dx.doi.org/10.31539/intecoms.v6i1.6071.

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Diseases that attack chili plants can be diagnosed early by observing symptoms or changes that occur in the leaves and fruit of the chili plant. However, diseases or pests that attack chili plants within a single plant can vary. In this study, YOLO v7 was used to perform leaf and chili segmentation on images, and the segmented results were then classified for chili plant disease using Deep Convolutional Neural Network (DCNN) Transfer Learning with the Fine Tuning method. The test results of the constructed model showed that the Yolo v7 segmentation accuracy was 0.970 on mAP50 when performing c

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Sitnikov, V. L., A. A. Strelenko, S. I. Kedich, and A. V. Komarova. "Socio-perceptual images as regulators of child-parental relations in foster families." Social Psychology and Society 12, no. 2 (2021): 129–47. http://dx.doi.org/10.17759/sps.2021120208.

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Objective. Definition of communications of I-images of mothers with He-images of the own children, I-images of the foster mothers with He-images of foster children became the purpose of our research; establishment of communications of I-images of the foster mothers with the child parental relation and interaction. Background. The problem of social and perceptual reflection is current because the number of families with receptions and the sponsored children grows. Quite often adoptive parents aren’t ready to adequate interaction with nonnative children and return them in the system of guardians

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Masykur, Fauzan, Kusworo Adi, and Oky Dwi Nurhayati. "Measurement of plant leaf area as a result of drone acquisition with arUco markers as a reference." E3S Web of Conferences 448 (2023): 02051. http://dx.doi.org/10.1051/e3sconf/202344802051.

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Drones or called Unmanned Aerial Vehicle is an unmanned aircraft technology that is controlled using a remote. Drones are able to enter various sectors including the agricultural, transportation, military and maritime sectors. In the agricultural sector, the use of drones is used to capture agricultural land as a dataset in determining object detection models in determining plant diseases. Drones fly over agricultural land to acquire various plant leaves with a variety of acquisition distances. The acquisition process is carried out at a measured time due to various considerations such as weat

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Hamdadur Rahman, Hasan Md Imran, Amira Hossain, Md Ismail Hossain Siddiqui, and Anamul Haque Sakib. "Explainable vision transformers for real‑time chili and onion leaf disease identification and diagnosis." International Journal of Science and Research Archive 15, no. 1 (2025): 1823–33. https://doi.org/10.30574/ijsra.2025.15.1.1163.

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Early identification of leaf diseases in chili and onion crops is crucial for maintaining agricultural productivity and reducing economic losses. This study proposes a transformer-based deep learning framework for the multi-class classification of common leaf diseases affecting chili and onion plants. It addresses challenges related to intra-class similarity, complex backgrounds, and variations in real-world imaging. We collected a curated dataset consisting of 13,989 high-resolution images—10,987 of chili leaves and 4,502 of onion leaves—from actual agricultural environments in Karnataka, Ind

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Hickman, Lisa Nichols. "Lead Me Beside Still Waters." Worldviews 19, no. 1 (2015): 34–50. http://dx.doi.org/10.1163/15685357-01901003.

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Artist Ena Swansea paints a provocative paradox in “One” from her “4 Seasons” quadtych: Is the child in the bathtub playfully holding a bubble, the orb of our global commons, or a crystal ball that portends an ominous future? As the viewer is confronted with the image of a child who, in the middle of an ordinary daily routine, is up to his armpits in a pool of blood red water, the question of water toxicity becomes central in the painting. Working from that image, this paper explores the interaction between water toxicity and Trisomy 21, proposing the need for a “precautionary principle” to gu

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Silva, Samuel Henrique, Arun Das, Adel Aladdini, and Peyman Najafirad. "Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 968–79. http://dx.doi.org/10.1609/icwsm.v16i1.19350.

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Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes. One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across mode

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Ak Entuni, Chyntia Jaby, Tengku Mohd Afendi Zulcaffle, Kismet Hong Ping, Amit Baran Sharangi, Tarun Kumar Upadhyay, and Mohd Saeed. "SMART AGRICULTURAL MONITORING SOLUTION FOR CHILLI LEAF DISEASES USING A LOW-COST KINECT CAMERA AND AN IMPROVED CNN ALGORITHM." Jurnal Teknologi 85, no. 5 (2023): 93–102. http://dx.doi.org/10.11113/jurnalteknologi.v85.19884.

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Chilli is extensively grown all over the globe and is particularly important as a food. One of the most difficult issues confronting chilli cultivation is the requirement for accurate identification of leaf diseases. Leaf diseases have a negative impact on chilli production quality, resulting in significant losses for farmers. Numerous Machine Learning (ML) and Convolution Neural Network (CNN) models have been developed for classifying chilli leaf diseases under uniform background and uncomplicated leaf conditions, with an average classification accuracy achieved. However, a diseased leaf usua

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Harahap, Jaffar Siddik, and Arnes Sembiring. "Klasifikasi Penyakit Tanaman Cabai Menggunakan Googlenet Pada Citra Daun." INCODING: Journal of Informatics and Computer Science Engineering 5, no. 1 (2025): 51–63. https://doi.org/10.34007/incoding.v5i1.832.

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Red chili pepper (Capsicum annuum L.) is a horticultural commodity that has high economic value, but its production is often hampered by plant disease attacks. To automatically detect diseases in chili leaves, this study uses a deep learning approach with GoogLeNet architecture and transfer learning techniques. This study aims to classify five types of chili leaf diseases, namely Healthy, Leaf Curl, Leaf Spot, Whitefly, and Yellowish, using a model initialized with pretrained weights from ImageNet. Three types of optimizers (Adam, RMSprop, and SGD) were tested to evaluate their effect on class

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Ayan, Chakraborty, Das Suvajeet, and Mondal Biplob. "Integrating Neural Network for Pest Detection in Controlled Environment Vertical Farm." Indian Journal of Science and Technology 15, no. 17 (2022): 829–38. https://doi.org/10.17485/IJST/v15i17.353.

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Abstract <strong>Background:</strong> An integrated system for creating and maintaining controlled environment ideal for vertical farming prototype is demonstrated. The requirement of optimal artificial light for different growth stages of tomato and chilli plants is studied in detail and CNN model-based method for detection and classification of Leaf disease is also developed. <strong>Methods:</strong> The artificial environment ensuring adequate artificial lighting, moisture, and minerals was create by implanting various sensors and actuators to the plant beds and connected in

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Sanagi, Tomomi. "Teachers' Misunderstanding The Concept Of Inclusive Education." Contemporary Issues in Education Research (CIER) 9, no. 3 (2016): 103–14. http://dx.doi.org/10.19030/cier.v9i3.9705.

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Teachers' misunderstanding the concept of inclusive education will not lead to good practices, rather make an exclusive environment for pupils with special educational needs in mainstream schools. This study clarified teachers' attitudes towards the image of inclusive education with conjoint analysis and cluster analysis. The participants for this study were 182 teachers who were from both mainstream schools and special schools. Their image about inclusive education was mainly dependent upon the organization of group and size of a group. The subfile summary of the conjoint analysis indicated t

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Rodrigues, Athena, Druvi Tendulkar, Jaysel Silveira, Surayya Akiwat, Vibhuti Talekar, and Ankit Kumar Tiwari. "Re-Unite: Missing Children Tracking Application." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4811. http://dx.doi.org/10.22214/ijraset.2023.54529.

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Abstract: The growing incidents of missing children are a cause of concern. Child trafficking and abduction are major factors that lead to missing cases. A large number of child trafficking victims are very young. When it comes to identifying missing persons or victims of disasters, one of the biggest challenges is the difficulty of obtaining accurate facial images. Often, the passage of time can cause a person's appearance to change significantly from the photos that their loved ones may have on hand. Additionally, in cases where a person has gone missing, there may be few or no photos availa

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TAM, Akshaya, PrasanthiSreeja P, J. Jayashankari, Aezeden Mohamed, Sodikova Iroda, and V. Vijayan. "Identification of Brain Tumor on Mri images with and without Segmentation using DL Techniques." E3S Web of Conferences 399 (2023): 04049. http://dx.doi.org/10.1051/e3sconf/202339904049.

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Brain cancer is a critical disease that results in the deaths of many individuals. Early detection and classification of brain tumors is essential for effective treatment and improved patient outcomes. However, current manual examination of MRI images for tumor detection can be time-consuming and imprecise. In this project, we propose a computer-based system that utilizes image processing techniques and convolutional neural networks (CNNs) for accurate and efficient brain tumor detection and classification. Our system involves several stages, including image pre-processing, segmentation, featu

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Muhamediyeva, Dilnoz, and Nozir Tukhtamurodov. "Diagnostics system for plant leaf diseases using photo images." E3S Web of Conferences 401 (2023): 04023. http://dx.doi.org/10.1051/e3sconf/202340104023.

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The subject under consideration is the issue of detecting agricultural crop diseases. The preliminary data for determining the phytosanitary status of cultivated plants are the images of their leaves. To tackle this issue, a model of diagnostic algorithms has been proposed, which involves creating a set of preferred characteristics and making diagnostic decisions based on comparing these features. The process of establishing the model of diagnostic algorithms has been outlined. The effectiveness of the proposed model has been demonstrated in diagnosing wheat diseases based on leaf images. A no

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J, Prasad, RubanKumar J, Thirumoorthi G, UdayaKeerthi V S, and Santhosh S M. "Machine Learning Based Automatic Leaf Diseases Detection." E3S Web of Conferences 399 (2023): 01001. http://dx.doi.org/10.1051/e3sconf/202339901001.

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The method for applying machine learning to automatically detect leaf diseases is presented in this paper. A convolutional neural network was used to extract pertinent features from leaf image datasets that included healthy and diseased leaves. The dataset was compiled and pre-processed. Accuracy, precision, and recall measures were used to assess the machine learning algorithm after it had been trained on the labeled dataset. According to the findings, the algorithm was very precise and recallable in its ability to detect leaf illnesses, making it a potential method for practical use. This st

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Medina, N., P. Vidal, R. Cifuentes, J. Torralba, and F. Keusch. "Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales." Revista de Teledetección, no. 52 (December 26, 2018): 41. http://dx.doi.org/10.4995/raet.2018.10916.

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<p>The <em>Araucaria araucana</em> is an endemic species from Chile and Argentina, which has a high biological, scientific and cultural value and since 2016 has shown a severe affection of leaf damage in some individuals, causing in some cases their death. The purpose of this research was to detect, from hyperspectral images, the individuals of the Araucaria species (<em>Araucaria araucana</em> (Molina and K. Koch)) and its degree of disease, by isolating its spectral signature and evaluating its physiological state through indices of vegetation and positioning te

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Muchlis, Ardi Zulfikar. "The Effects of Various Doses of Azadirachta indica A. Juss. Seed Cake against Aphis gossypii (Glover) and Growth Characters of Red Chili Plants (Capsicum annuum L.)." CROPSAVER - Journal of Plant Protection 4, no. 1 (2021): 15. http://dx.doi.org/10.24198/cropsaver.v4i1.33780.

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Neem plant is used as plant-based insecticide because all parts of the plant have insecticides activities. The utilization of neem plants as plant-based insecticides is generally only in the seed parts, but the extraction of neem seed extract has not been used because it is considered as waste. The utilization of neem seed cake as a natural insectiside is one way to recycle neem seed cake which is known to contain active ingredients of neem seed oil. Apart from its use as an insecticide, neem seed cake can also be used as an organic fertilizer. This study aimed to determine the effect of appli

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Petrenko-Lysak, Alla. "Visual essay – «Last day food»." Text and Image: Essential Problems in Art History, no. 2 (2023): 54–73. http://dx.doi.org/10.17721/2519-4801.2023.2.04.

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John Berger pioneered the genre of the visual essay. Inspired by his work, we crafted our own essay exploring the representation of food and nutrition in the context of a global catastrophe. To delve into the visualization of food in the final days of humanity, we specifically selected movies depicting post-apocalyptic scenarios. The visual essay comprises scenes that highlight various aspects of food, including its essence, the quest for it, consumption, the relentless pursuit, and its allure as a temptation. The essay concludes with an AI-generated image that unexpectedly features a child. I

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García, Nicolás, Arón Cádiz-Véliz, Macarena Villalobos, and Vanezza Morales. "Taxonomic novelties in Haplopappus (Asteraceae, Astereae) from Chile." PhytoKeys 237 (January 29, 2024): 201–18. http://dx.doi.org/10.3897/phytokeys.237.114461.

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Two new species of Haplopappus (Asteraceae) from central Chile are described in this article. Haplopappus colliguayensissp. nov. is restricted to La Chapa hill, Colliguay, Valparaíso Region, and is most similar to H. undulatus but differs from the latter in its stem indumentum, leaf shape and margin, and synflorescence arrangement. Haplopappus teillierisp. nov. has been recorded from four high-Andean localities in the Choapa, Petorca, Rocín and Aconcagua river basins, and is most similar to H. punctatus but differs from the latter in its leaf length and margin, number of peduncles per twig, wi

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Mortazavi, Nina, Philippe Chauffet-Riffaud, Frédérique Archambaud, and Alain Prigent. "Biliary Leak in a Child After Liver Transplant and Value of Delayed Images." Clinical Nuclear Medicine 33, no. 1 (2008): 44–45. http://dx.doi.org/10.1097/rlu.0b013e31815c5115.

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N V, Sheenu. "Chilli Plant Disease Detection and Classification using DenseNet CNN Approach." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4469–77. http://dx.doi.org/10.22214/ijraset.2021.36171.

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To fulfil the food requirement and economic growth, farming plays a very important role. Thus Farmers are the most important people in the world. Be it the smallest or the largest country, Because of them only we are able to live on the planet. Precision agriculture is the new trending term in the field of technology whose main motive is to reduce the workload of the farmers and increase the productivity of the farms by using technologies. So the aim of this work is to detect the disease of the plant by classifying their leaves using deep learning algorithm. For this work chilli plants are con

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García, Nicolás, Arón Cádiz-Véliz, Macarena Villalobos, and Vanezza Morales. "Taxonomic novelties in Haplopappus (Asteraceae, Astereae) from Chile." PhytoKeys 237 (January 29, 2024): 201–18. https://doi.org/10.3897/phytokeys.237.114461.

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Two new species of <i>Haplopappus</i> (Asteraceae) from central Chile are described in this article. <i>Haplopappus colliguayensis</i> sp. nov. is restricted to La Chapa hill, Colliguay, Valparaíso Region, and is most similar to <i>H. undulatus</i> but differs from the latter in its stem indumentum, leaf shape and margin, and synflorescence arrangement. <i>Haplopappus teillieri</i> sp. nov. has been recorded from four high-Andean localities in the Choapa, Petorca, Rocín and Aconcagua river basins, and is most similar to <i>H. punctatus</i> but differs from the latter in its leaf length and mar

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М, Анара. "ПСИХОАНАЛИЗ ХӨДӨЛГӨӨН, ТҮҮНИЙ ТҮҮХ-ХӨГЖИЛ". Philosophy and Religious Studies 13, № 65(371) (2012): 68–78. https://doi.org/10.22353/prs20121.8.

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This paper will introduce psychoanalytic movement and its development through its successors' biography and their theory. Since 1902, the psychoanalytic movement started to extend its circle actively. In this early period of the history of psychoanalytic movement, the central event has been taken between K.G.Jung, who was the center of the Zurich circle, and S.Freud regarding to their friendship and a conflict between their theoretical status on the libido theory. To France, psychoanalysis was introduced by Henri Claude in 1920. In this paper, I introduce J.Lacan, representing him as one of th

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SHIOTA, Shoichi, Shin-ichi OURA, and Mariko MATSUMOTO. "Maladaptive fantasy predicts negatively distorted self and other mental representation: A consideration of child abuse from psycho/neuro/biological perspectives." Environment and Social Psychology 9, no. 7 (2024): 2079. http://dx.doi.org/10.59429/esp.v9i7.2079.

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Child abuse is a prevalent public health issue with one half of children worldwide experiencing some form of violence. Child abuse is associated with a myriad of impacts across the lifespan such as mental and physical illness, academic performance, and employment. For this reason, individual’s psychological functions such as emotional regulation, autobiographical memory and self, and psychological connection with others are changed by child abuse. However, to best of our knowledge, there is still much unknowns about the mechanism underlying these changes. In this article, we focusing on the re

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