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Journal articles on the topic 'Diseased and Healthy leaf'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Jadhav, Sachin B., Vishwanath R. Udup, and Sanjay B. Patil. "Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4077. http://dx.doi.org/10.11591/ijece.v9i5.pp4077-4091.

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Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work present a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by

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Sachin, B. Jadhav, R. Udupi Vishwanath, and B. Patil Sanjay. "Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4077–91. https://doi.org/10.11591/ijece.v9i5.pp4077-4091.

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Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work presents a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by

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Natesan, Balaji, Anandakumar Singaravelan, Jia-Lien Hsu, Yi-Hsien Lin, Baiying Lei, and Chuan-Ming Liu. "Channel–Spatial Segmentation Network for Classifying Leaf Diseases." Agriculture 12, no. 11 (2022): 1886. http://dx.doi.org/10.3390/agriculture12111886.

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Agriculture is an important resource for the global economy, while plant disease causes devastating yield loss. To control plant disease, every country around the world spends trillions of dollars on disease management. Some of the recent solutions are based on the utilization of computer vision techniques in plant science which helps to monitor crop industries such as tomato, maize, grape, citrus, potato and cassava, and other crops. The attention-based CNN network has become effective in plant disease prediction. However, existing approaches are less precise in detecting minute-scale disease

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Rehman, Asad, Yasir Iftikhar, Mustansar Mubeen, Muhammad Ahmad Zeshan, Ashara Sajid, and Aqleem Abbas. "EFFECT OF GUMMOSIS CAUSED BY PHYTOPHTHORA SPP. ON LEAF AREA AND TRUNK SIZE OF CITRUS IN DISTRICT SARGODHA, PAKISTAN." Plant Protection 6, no. 1 (2022): 1–9. http://dx.doi.org/10.33804/pp.006.01.4017.

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Gummosis disease of citrus, caused by Phytophthora spp. is one of the most devastating diseases of citrus in Pakistan. Considering the menace of this disease, the present study was conducted to determine the disease incidence and its effect on the trunk size and leaf area of affected plants. A comprehensive survey was conducted in 75 citrus orchards in 3 tehsils (Sargodha, Bhalwal, and Kot Momin) of the district Sargodha for the prevalence of the disease. The diseased plants were marked from each orchard, trunk size was measured and leaf area was recorded using leaf area meter. The leaves from

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Kumar, Raj. "Smart Plantation Forecasting and Prevention of Plant Disease." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47409.

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Abstract— Crop diseases are a noteworthy risk to sustenance security, however their quick distinguishing proof stays troublesome in numerous parts of the world because of the non attendance of the important foundation. Emergence of accurate techniques in the field of leaf-based image classification has shown impressive results. This paper makes use of Random Forest in identifying between healthy and diseased leaf from the data sets created. Our proposed paper includes various phases of implementation namely dataset creation, feature extraction, training the classifier and classification. The c

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Y, Mrs Swathi. "PLANT DISEASE DETECTION USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31443.

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Crop diseases pose a significant threat to food security, but their timely detection is challenging in many regions due to a lack of necessary infrastructure. Recent advancements in leaf-based image classification have yielded promising outcomes. This study leverages Random Forest to differentiate between healthy and diseased leaves using newly created datasets. The proposed methodology includes dataset generation, feature extraction, classifier training, and image classification. Diseased and healthy leaf datasets are collectively trained using Random Forest for accurate classification. Featu

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Nadeem, A., T. Mehmood, M. Tahir, S. Khalid, and Z. Xiong. "First Report of Papaya Leaf Curl Disease in Pakistan." Plant Disease 81, no. 11 (1997): 1333. http://dx.doi.org/10.1094/pdis.1997.81.11.1333b.

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Papaya plants with virus-disease-like symptoms were observed in back yards and commercial groves in Multan, Pakistan. Leaves of the diseased plants displayed downward curling and thickened, dark green veins. Leaf-like enations grew from the base of the diseased leaves. These symptoms are similar to those of cotton leaf curl disease. In addition, diseased papayas were stunted and distorted. Leaf extracts from 3 diseased and 2 healthy papayas were tested in enzyme-linked immunosorbent assay against antibodies to geminiviruses. SCRI-52 and SCRI-60, two monoclonal antibodies to Indian cassava mosa

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Parrella, Rafael Augusto da Costa, João Bosco dos Santos, Nádia Nardely Lacerda Durães Parrella, and Diego Velásquez Faleiro e. Silva. "Evaluation efficiency of severity of angular leaf spot in common bean based on diseased and healthy leaf area." Crop Breeding and Applied Biotechnology 13, no. 3 (2013): 178–85. http://dx.doi.org/10.1590/s1984-70332013000300005.

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This study compared severity of angular leaf spot in common bean lines, based on the healthy and diseased leaf area, and the graded scale. We used 12 common bean lines in the dry and rainy seasons. Two contiguous experiments were conducted in each season, with and without chemical control of the pathogen. We evaluated the percentage of the healthy and diseased leaf area; severity based on a graded scale and the area under the disease progress curve; and yield. The diseased or healthy leaf area is efficient to evaluate the severity of angular leaf spot with a sample of 20 to 30 leaflets per plo

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Mohana Priya. C, Et al. "Customized Semantic Segmentation for Enhanced Disease Detection of Maize Leaf Images." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 31–37. http://dx.doi.org/10.17762/ijritcc.v11i11.9074.

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Maize leaf images are affected by various diseases. Though many image processing techniques are available to identify diseased segment of a diseased maize leaf image proper methodology to segment every chunk in the leaf as disease, shadow, healthy and background using a single methodology is still in search of. So, a single line of attack is availed using Semantic Segmentation for diseased maize Leaf images through which every pixel in an image is equated to a class. Initially multiple classes in the maize leaf images are Labeled and trained. ImagedataStore and PixelLabelDatastore are used to

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Akhalifi, Yuris, and Agus Subekti. "Bell Pepper Leaf Disease Classification Using Fine-Tuned Transfer Learning." Jurnal Elektronika dan Telekomunikasi 23, no. 1 (2023): 55. http://dx.doi.org/10.55981/jet.546.

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Leaf diseases of plants are common worldwide. Using image processing, farmers could spot diseases in pepper plants more rapidly and get advice from plant disease experts. In this paper, researchers developed a Transfer Learning classification model for bell pepper leaf disease, with the Transfer Learning model trained on images of healthy and diseased bell pepper leaves. Classification of healthy and diseased bell pepper leaves has been carried out, and fine-tuned Transfer Learning has been applied using several pre-trained CNN models. To achieve the best outcome, four pre-trained models, incl

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Mori, Noriyuki, Hiroki Naito, and Fumiki Hosoi. "Application of a Latent Diffusion Model to Plant Disease Detection by Generating Unseen Class Images." AgriEngineering 6, no. 4 (2024): 4901–10. https://doi.org/10.3390/agriengineering6040279.

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Deep learning-based methods have proven to be effective for various purposes in the agricultural sector. However, these methods require large amounts of labelled data, which are difficult to prepare and preprocess. To overcome this problem, we propose the use of a latent diffusion model for plant disease detection by generating unseen class images. In this study, we used images of healthy and diseased grape leaves as training datasets and utilized the latent diffusion model, known for its superior performance in image generation, to generate images of diseased apple leaves that were not includ

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Prasad, W. Bhombe, and Shirish V. Pattalwar Dr. "Proposed Methodology for Recognition of Plant Diseases by Leaf Image Classification Using Machine Learning." International Journal of Research in Computer & Information Technology (IJRCIT), 7, no. 4 (2022): 1–7. https://doi.org/10.5281/zenodo.7180889.

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Crop diseases are a noteworthy risk to sustenance security, however their quick distinguishing proof stays troublesome in numerous parts of the world because of the non-attendance of the important foundation. Emergence of accurate techniques in the field of leaf-based image classification has shown impressive results. This paper makes use of Random Forest in identifying between healthy and diseased leaf from the data sets created. Our proposed paper includes various phases of implementation namely dataset creation, feature extraction, training the classifier and classification. The created dat

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13

Kamble, Ms D. R., Ms Snehal Krishna Gadale, Ms Dhanashri Nitin Pawar, Ms Gauri Dhanaji Shedage, and Ms Mukti Mahajan. "Leaf Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 160–64. http://dx.doi.org/10.22214/ijraset.2024.58699.

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Abstract: The agricultural sector plays a crucial role in sustaining the world's growing population. However, the prevalence of plant leaf diseases can significantly impact crop yields and quality. This project leverages the power of deep learning techniques to develop an automated system for the early detection of plant leaf diseases. By utilizing a large dataset of annotated leaf images and state-of-the-art convolutional neural networks (CNNs), this research aims to accurately identify and classify various plant leaf diseases, including but not limited to fungal, bacterial, and viral infecti

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Evert, D. R., and D. A. Smittle. "Phony Disease Influences Peach Leaf Characteristics." HortScience 24, no. 6 (1989): 1000–1002. http://dx.doi.org/10.21273/hortsci.24.6.1000.

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Abstract Midshoot peach [Prunus persica (L.) Batsch.] leaves were collected in 1984 and 1985 from phony-diseased [presumably infected with Xylella fastidiosa (Wells et al.)] and healthy trees of several cultivars at intervals during the summer. Leaves were evaluated for specific chlorophyll content, specific leaf weight, and color (lightness, hue, and saturation). The darker green of diseased trees reported previously could not be attributed to the quantitative changes in the leaf characteristics measured in this study. Midshoot leaves from diseased trees were more yellow and less green than m

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Wang, Shiwei, Yu Tan, Shujiang Li, and Tianhui Zhu. "Structural and Dynamic Analysis of Leaf-Associated Fungal Community of Walnut Leaves Infected by Leaf Spot Disease Based Illumina High-Throughput Sequencing Technology." Polish Journal of Microbiology 71, no. 3 (2022): 429–41. http://dx.doi.org/10.33073/pjm-2022-038.

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Abstract Leaf-associated microbiota is vital in plant-environment interactions and is the basis for micro-ecological regulation. However, there are no studies on the direct differences in microbial community composition between disease-susceptible and healthy walnut leaves. This study collected five samples of healthy and infected leaves (all leaves with abnormal spots were considered diseased leaves) from May to October 2018. Differences in fungal diversity (Chao1 index, Shannon index, and Simpson index) and community structure were observed by sequencing and analyzing diseased and healthy le

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Rustam, Rustam, Rita Noveriza, Siti Khotijah, et al. "Convolution Neural Network Approach for Early Identification of Patchouli Leaf Disease in IndonesiaConvolution Neural Network Approach for Early Identification of Patchouli Leaf Disease in Indonesia." Journal of Image and Graphics 12, no. 2 (2024): 137–44. http://dx.doi.org/10.18178/joig.12.2.137-144.

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Indonesia is the largest supplier of patchouli oil in the world market, contributing 80%–90%. Most patchouli oil products are exported in the perfume, cosmetics, pharmaceutical, antiseptic, aromatherapy, and insecticide industries. The emergence of patchouli leaf disease significantly reduced the production of wet, dry, oil, and patchouli alcohol. Therefore, selecting patchouli cuttings (seedlings) that are entirely healthy and disease-free is very important to prevent disease transmission from one area to another. In addition, the selection of disease-free seeds is also essential to prevent t

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17

Serrago, Román A., and Daniel J. Miralles. "Source limitations due to leaf rust (caused by Puccinia triticina) during grain filling in wheat." Crop and Pasture Science 65, no. 2 (2014): 185. http://dx.doi.org/10.1071/cp13248.

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Late foliar diseases (especially leaf rust) reduce assimilate supply during post-anthesis, determining fewer assimilates per grain and thereby inducing grain weight reductions. Although the assimilate reduction hypothesis is the most accepted to explain decreases in grain weight due to late foliar diseases, it has not been clearly established whether those reductions could be completely ascribed to source limitations or whether diminished grain weight could be the consequence of reductions in grain weight potential. The objective of this work was to determine whether grain weight reductions du

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Venkataramana, R. "LEAF DISEASE DETECTION AND REMEDY RECOMMENDATION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28585.

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Agriculture impacts life and economic status of the people. Improper management of diseases results in annual loss of agricultural yield which will have serious effects on the quality, quantity and productivity if no proper care is taken. By using some automatic technique such as image processing, detection of leaf disease is quite significant and beneficial. The use of the most utilized deep learning classification mechanism, Convolutional Neural Network, helps in this regard. This paper proposes an innovative machine learning approach for automated leaf disease detection. By utilizing image

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Pandian J., Arun, Kanchanadevi K., N. R. Rajalakshmi, and G.Arulkumaran. "An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection." Computational Intelligence and Neuroscience 2022 (September 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/5102290.

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In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using a combined plant leaf disease image dataset. Scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to create the augmentation data of the plant leaf disease image dataset. The dataset consisted of 103 diseased and healthy image classes of 22 plants and 154,500 images of healt

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Sannakki, Sanjeev S., Vijay S. Rajpurohit, V. B. Nargund, and R. Arunkumar. "Disease Identification and Grading of Pomegranate Leaves Using Image Processing and Fuzzy Logic." International Journal of Food Engineering 9, no. 4 (2013): 467–79. http://dx.doi.org/10.1515/ijfe-2012-0241.

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AbstractPlant diseases cause major losses to several agricultural and horticultural crops around the World. Therefore, methods for proper diagnosis of diseases found in any parts of the plant body play a crucial role in disease management. In the past few decades, many methods and techniques of image processing and soft computing are applied on a number of plants to diagnose and treat variety of plant diseases. Hence, the present work is aimed to develop an automated system that results in three major outcomes for a leaf image. They are disease identification, disease grading and treatment adv

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Pasalkar, Jayashree, Ganesh Gorde, Chaitanya More, Sanket Memane, and Vaishnavi Gaikwad. "Potato Leaf Disease Detection Using Machine Learning." Current Agriculture Research Journal 11, no. 3 (2024): 949–54. http://dx.doi.org/10.12944/carj.11.3.23.

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Potato is one of the most important crops worldwide, and its productivity can be affected by various diseases, including leaf diseases. Early detection and accurate diagnosis of leaf diseases can help prevent their spread and minimize crop losses. In recent years, Convolutional Neural Networks (CNNs) have shown great potential in image classification tasks, including disease detection in plants. In this study, we propose a CNN-based approach for the prediction of potato leaf diseases. The proposed method uses a pre-trained CNN model, which is fine-tuned on a dataset of potato leaf images. The

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Mohan, Dr K. Madan. "LEAF DISEASE PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–6. http://dx.doi.org/10.55041/ijsrem27703.

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Leaf diseases are a major problem in agriculture, causing significant losses in crop yield and quality. Early detection of leaf diseases is essential for effective management, but it can be difficult and time- consuming to do manually. In recent years, there has been growing interest in the use of machine learning and computer vision techniques for leaf disease prediction. These techniques can be used to automatically extract features from leaf images that are indicative of disease, and then use these features to train a classifier that can distinguish between healthy and diseased leaves. Seve

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Sarangi, Piyush Kumar, and Er Jagannath Ray. "Plant Leaf Disease Detection Using Machine Learning Algorithm." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51361.

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This project focuses on detecting plant leaf diseases using machine learning. Farmers often face crop loss due to diseases, and early detection can help prevent this. The system takes images of plant leaves, processes them to extract important features (like color and shape), and then uses a machine learning model to identify if the leaf is healthy or has a disease. We use algorithms like Support Vector Machine (SVM) or Convolutional Neural Network (CNN) to train the model with examples of diseased and healthy leaves. Once trained, the system can accurately predict the disease from a new leaf

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V. Rajagoval and B. Sumathykuttyamma. "IDENTIFICATION OF ROOT (WILT) DISEASED COCONUT PALMS BEFORE VISUAL SYMPTOM EXPRESSION." CORD 5, no. 02 (1989): 34. http://dx.doi.org/10.37833/cord.v5i02.227.

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Determination of stomatal resistance (rs) and leaf water potential (ψ) were employed as techniques to distinguish coconut palms (Cocos nucifera L.) devoid of foliar symptoms ('apparently healthy') from those with symptorns of wilt disease viz. flaccidity, yellowing and necrosis ('wilt diseased'). Infected palms are characterized by low stomatal diffusive resistance and reduction in leaf water potential. Among the apparently healthy palms, some exhibited high rs and high ψ, characteristic of truly healthy palms, while others had the trend similar to the 'wilt' diseased palms. The latter group o

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Lopes, Daniela B., and Richard D. Berger. "The Effects of Rust and Anthracnose on the Photosynthetic Competence of Diseased Bean Leaves." Phytopathology® 91, no. 2 (2001): 212–20. http://dx.doi.org/10.1094/phyto.2001.91.2.212.

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The effects of rust (caused by Uromyces appendiculatus) and anthracnose (caused by Colletotrichum lindemuthianum) and their interaction on the photosynthetic rates of healthy and diseased bean (Phaseolus vulgaris) leaves were determined by gas-exchange analysis, in plants with each disease, grown under controlled conditions. The equation Px/P0 = (1 - x)β was used to relate relative photosynthetic rate (Px/P0) to proportional disease severity (x), where β represents the ratio between virtual and visual lesion. The β values obtained for rust were near one, indicating that the effect of the patho

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Purnama, Adi, Esa Fauzi, and Bagus Alit Prasetyo. "Implementing PSO-based Image Segmentation for Detecting Sweet Potato Leaf Disease." International Journal of Multidisciplinary Approach Research and Science 3, no. 02 (2025): 447–57. https://doi.org/10.59653/ijmars.v3i02.1482.

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Sweet potato (Ipomoea batatas) is an important global crop, but its production is threatened by various leaf diseases, requiring accurate and efficient disease detection methods. Traditional manual inspection is labor-intensive and error-prone, making automated image processing techniques a promising alternative. This study implements Particle Swarm Optimization (PSO)-based image segmentation to detect diseased leaf regions by optimizing threshold selection in HSV color space. In the classification phase, leaves are classified into healthy and diseased classes using a Euclidean distance-based

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Vishnu Prabhakar. V, Dr. N. Sudha. "Segmentation Algorithms For Accurate Decision Of Banana Leaf Diseases In Precision Agriculture." Nanotechnology Perceptions 20, no. 4 (2024): 806–16. https://doi.org/10.62441/nano-ntp.v20i4.5270.

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Banana cultivation plays a crucial role in the agricultural economy, but its productivity is significantly affected by various leaf diseases. Early and accurate identification of banana leaf diseases is essential for effective disease management and yield optimization. This study presents an automated approach for detecting and classifying banana leaf diseases using a segmentation algorithm. The proposed method involves image preprocessing, segmentation, and feature extraction techniques to isolate diseased regions from healthy leaf areas. By applying advanced image processing and machine lear

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Khan, Khalil, Rehan Ullah Khan, Waleed Albattah, and Ali Mustafa Qamar. "End-to-End Semantic Leaf Segmentation Framework for Plants Disease Classification." Complexity 2022 (May 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/1168700.

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Pernicious insects and plant diseases threaten the food science and agriculture sector. Therefore, diagnosis and detection of such diseases are essential. Plant disease detection and classification is a much-developed research area due to enormous development in machine learning (ML). Over the last ten years, computer vision researchers proposed different algorithms for plant disease identification using ML. This paper proposes an end-to-end semantic leaf segmentation model for plant disease identification. Our model uses a deep convolutional neural network based on semantic segmentation (SS).

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SERTTAŞ, Soydan, and Emine DENİZ. "Disease detection in bean leaves using deep learning." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 65, no. 2 (2023): 115–29. http://dx.doi.org/10.33769/aupse.1247233.

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The care and health of agricultural plants, which are the primary source for people to eat healthily, are essential. Disease detection in plants is one of the critical elements of smart agriculture. In parallel with the development of artificial intelligence, advancements in smart agriculture are also progressing. The development of deep learning techniques positively affects smart farming practices. Today, using deep learning and computer vision techniques, various plant diseases can be detected from images such as photographs. In this research, deep learning techniques were used to detect an

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Altukhov, V. G. "Plant leaf images computerized segmenation." IOP Conference Series: Earth and Environmental Science 957, no. 1 (2022): 012002. http://dx.doi.org/10.1088/1755-1315/957/1/012002.

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Abstract In this paper the comparison of RGB, HSV and CIELab color spaces is considered in view of diseased leaf images segmentation by color thresholding method. In such tasks HSV and CIELab outperform RGB. Thresholding method based upon HSV or CIELab color spaces can be applied to measuring leaves total area, diseased and healthy surfaces area, as well as dataset composing in machine learning.

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Tagare, Mohammad M., Urmila R. Pol, Parashuram S. Vadar, and Tejashree T. Moharekar. "Detection of Jackfruit Leaf Disease Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1871–76. https://doi.org/10.22214/ijraset.2025.68592.

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Abstract: The detection of diseases in agricultural crops plays a critical role in maintaining healthy yields. Jackfruit (Artocarpus heterophyllus), a tropical fruit, is susceptible to various diseases that impact its leaves. Timely disease detection can significantly reduce crop loss and improve the quality of the harvest. This paper proposes a system for detecting jackfruit leaf diseases using machine learning (ML) and deep learning (DL) techniques. A dataset of healthy and diseased jackfruit leaf images is used to train both traditional ML algorithms (Random Forest) and DL models (Convoluti

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Md. Abdullah Mandal, Sumaiya Siddika Khushi, Zahid Hassan, et al. "Classification of Radish, Radish Leaf and Potato Leaf Disease Using Deep Learning Algorithm: Study and Accuracy Measurement." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 521–35. https://doi.org/10.32996/jcsts.2025.7.7.58.

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Radish and potato are important root vegetables that are extensively grown for their nutritional and economic importance. However, foliar diseases drastically impair productivity and quality. Early detection of these diseases is crucial for prompt intervention and successful crop management. This study uses Deep Learning based classification approach to automatically detect diseases in radishes, radish leaves and potato leaves using image data. The dataset consists of 9 distinct classes: healthy radish, healthy radish leaves, healthy potato leaves and several diseased categories. The radish le

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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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Nikesh, M., D. Rohini, S. Shaankari, M. Bharathi, and T. Aditya Sai Srinivas. "Verdant Vision: CNNs Revolutionizing Plant Leaf Disease Identification." Journal of Computer Systems, Virtualization and Languages 1, no. 2 (2024): 1–6. http://dx.doi.org/10.48001/jocsvl.2024.121-6.

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The advancement of technology has enabled the accurate and efficient detection of plant diseases, demonstrating the use of machine learning, especially Convolutional Neural Networks, which have become widely popular. Using the models of the CNN, it is realistic to create an application that identifies a disease based on photographs of the plants with the help of textures, leaf spots, sheen alterations, and other features. Since Convolutional Neural Networks are trained with large samples of diseased and healthy plant pictures, they are more adaptable to new unseen conditions. Therefore, medica

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K R, Krupa Prasad, Sunil Kumar R, Harshitha B R, Manoj Kumar M, and Raghavendra Rajesh Yaragatti. "Leaf Disease Detection using Convolutional Neural Network." Journal of Ubiquitous Computing and Communication Technologies 6, no. 4 (2025): 397–406. https://doi.org/10.36548/jucct.2024.4.006.

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For the past two decades, the imbalance between food supply and population growth has been a major concern. Agriculture plays an important role in human development, and technological improvements have significantly contributed to this process. In this study, Convolutional Neural Networks (CNNs) will be utilized to identify plant leaf diseases based on leaf images. The objective is to develop an application that accurately classifies plant images as healthy or diseased. This will be achieved by collecting and preprocessing a dataset of damaged and healthy plant images under varying watering co

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Nagaveni, B. Biradar, P. Farida, Yashoda, R. Sneha, and T. Tejeshwari. "Tomato Leaf Disease Detection Using Deep Learning and Flask." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 4, no. 4 (2025): 23–27. https://doi.org/10.5281/zenodo.15244627.

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Tomato plants are vulnerable to various leaf diseases that reduce crop yield and quality. This project uses deep  learning, specifically a Convolutional Neural Network (CNN), to detect and classify tomato leaf diseases accurately.  A dataset containing images of healthy and diseased leaves is used for training and validation. The model is deployed  via a Flask web application, enabling users to upload leaf images and receive instant diagnosis. This system provides  an accessible and cost-effective tool for early disease detection. It aims to support farmers and improve toma

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S, Jeyalakshmi, and Radha R. "CLASSIFICATION OF TOMATO DISEASES USING ENSEMBLE LEARNING." ICTACT Journal on Soft Computing 11, no. 4 (2021): 2408–15. http://dx.doi.org/10.21917/ijsc.2021.0343.

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A Plant disease is any dysfunction of a plant, caused by living organisms, which affects the quality and quantity of yield. These symptoms are visually shown on the plant leaves. This paper discusses classification of Tomato diseases such as Late Blight, Septoria Leaf Spot and Yellow leaf curl virus while distinguishing the healthy leaf at the same time. An experimental sample size of 1817 was considered in conducting this study. This work differentiates diseased tomato leaf images with healthy leaf images. The classifiers Random Forest, Multilayer Perceptron Neural Network and Support Vector

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Ms.K.T.Bhandwalkar, Anuj Sawant, Pawan Varpe, Satyajeet Sanas, and Pratiksha Raut. "Detection of Tomato Leaf Diseases for Agro-Based Industries Using Deepnet Network." Journal of Advancement in Big Data Science and Data Analysis 1, no. 2 (2025): 1–7. https://doi.org/10.5281/zenodo.15605901.

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<em>The detection and classification of tomato leaf diseases play a critical role in ensuring healthy crop production and minimizing agricultural losses. This research focuses on the development of an efficient and accurate system for disease identification using a DeepNet network architecture. The proposed approach leverages advanced deep learning techniques to identify common diseases such as early blight, late blight, and leaf mold from tomato leaf images. A curated dataset comprising healthy and diseased leaf samples was preprocessed and augmented to enhance model robustness. The DeepNet m

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Mr, Pramod K., and VR Nagarajan Dr. "Detection and Recognition of Paddy Leaf Diseases Using Image Processing." International Journal for Research in Engineering Application & Management (IJREAM) 08, no. 10 (2023): 007–11. https://doi.org/10.35291/2454-9150.2023.0002.

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The importance of agriculture to humanity cannot be denied. The need for food is rising in tandem with population growth; hence production should be boosted as much as possible. In order to accomplish this, crops need to be safeguarded against bacterial, viral, and fungal infections. A fast and precise diagnosis of illnesses in paddy leaves enables the timely initiation of agricultural practises, which greatly lowers economic losses. The paddy leaf disease was identified and classified for this purpose using image processing techniques. The segmentation of the diseased region, the non-diseased

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Evert, Dean R. "Influence of Phony Disease of Peach on Stem Hydraulic Conductivity and Leaf Xylem Pressure Potential." Journal of the American Society for Horticultural Science 112, no. 6 (1987): 1032–36. http://dx.doi.org/10.21273/jashs.112.6.1032.

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Abstract Stem hydraulic conductivity of peach [Prunus persica (L.) Batsch] was lower in trees with phony disease than in healthy trees. This lower conductivity occurred in 1- to 4-year stems, in five cultivars, in two pruning systems, and from June through October. Leaf xylem pressure potential was lower in trees with phony disease than in healthy trees in each of the five cultivars tested and from June through September. The reduction in pressure potential in diseased trees exceeded any variations in pressure potential with cultivar or month. The area of functional xylem stained by dye was vi

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T. A. A., Enow, Ngalle H. B., and Ngonkeu M. E. L. "Automated Estimation of Plant Leaf Disease Severity Using Classical Image Segmentation Techniques." Biotechnology Journal International 29, no. 2 (2025): 59–76. https://doi.org/10.9734/bji/2025/v29i2772.

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Aim: This study aimed to propose a computationally cost-effective method for automated estimation of plant leaf disease severity in resource-limited settings. Study Design: The performance of four image segmentation algorithms—global thresholding, adaptive thresholding, Otsu thresholding, and edge detection—was evaluated using nine curated images of disease-affected leaves from tomato, bell pepper, and potato plants. Each image was segmented into healthy and diseased regions, and quantitative metrics—including diseased pixel counts, percentage of affected area, healthy-to-diseased ratios, and

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Rahmanto, Oky, Veri Julianto, and Ahmad Rusadi Arrahimi. "Evaluating Random Forest Algorithm: Detection of Palm Oil Leaf Disease." Brilliance: Research of Artificial Intelligence 4, no. 2 (2024): 919–24. https://doi.org/10.47709/brilliance.v4i2.4798.

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This research investigates the application of machine learning techniques for detecting diseases in oil palm leaves, utilizing a dataset of 1,119 images sourced from plantations in the Tanah Laut district. The dataset comprises 488 diseased and 631 healthy leaf samples, which were carefully cropped to isolate leaf areas and labeled with the assistance of domain experts. For feature extraction, both Lab and RGB color spaces were considered, alongside Haralick texture features, resulting in a total of eleven features per pixel. To reduce dimensionality and select relevant features, Principal Com

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Kumari, Chanrika, Dibyanshu Singh, Dr Vikas Singhal, Gyanendra Kumar, and Dr Ajay Sahu. "End to End Leaf Detection Using Convolutional Neural Network." International Journal of Innovative Research in Advanced Engineering 11, no. 11 (2024): 825–33. https://doi.org/10.26562/ijirae.2024.v1111.06.

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Global food security is seriously threatened by crop diseases, yet in many places, it is still difficult to identify them quickly because of a lack of infrastructure. In tackling this problem, recent developments in leaf-based image categorization methods have produced encouraging outcomes. This study investigates how to differentiate between healthy and unhealthy leaves using curated datasets using the Random Forest algorithm. The development of the dataset, feature extraction, classifier training, and classification are the various stages of the implementation process. To efficiently disting

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Monigari, Vaishnavi. "Plant Leaf Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1295–305. http://dx.doi.org/10.22214/ijraset.2021.36582.

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The Indian economy relies heavily on agriculture productivity. A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity. Currently, more and more attention has been paid to plant diseases detection in monitoring the large acres of crops. Monitoring the health of the plants and detecting diseases is crucial for sustainable agriculture. Plant diseases are challen

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Gupta, Sahil, Vivek Pandey, Pravesh Pandey, Mukul Verma, and Hasib Shaikh. "Leaf Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1603–12. http://dx.doi.org/10.22214/ijraset.2023.50439.

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Abstract: Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduce

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Mishra, R. R., and R. S. Kanaujia. "Studies on phyllosphere Fungi. IV. Effect of magnesium chloride on phyllosphere population of virus infected (PVX) and healthy plants of Lycopersicum esculentum Mili. cv. Best of all." Acta Societatis Botanicorum Poloniae 43, no. 2 (2015): 213–20. http://dx.doi.org/10.5586/asbp.1974.020.

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Phyllosphere and phylloplane mycoflora of healthy and potato virus X (PVX) infected plants of <i>Lycopesicum esculentum</i> in relation to the treatment of different concentrations of magnesium chloride has been investigated. 250 ppm MgO<sub>2</sub> level resulted to the maximum fungal population on the leaf surface of healthy and diseased plants. 125 ppm concentration of MgO<sub>2</sub>. on the other hand favoured the maximum fungal colonization on phylloplane region in both healthy and diseased plants. In both, healthy and diseased plants, 125 ppm concentr

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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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B G, Jagadeesha, Ramesh Hegde, and Ajith Padyana. "Black pepper leaf disease detection using deep learning." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 897–907. https://doi.org/10.53894/ijirss.v8i2.5389.

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Advances in deep learning techniques have achieved spectacular success in the detection of plant diseases. A new method for detecting black pepper leaf disease using deep learning was proposed. In the proposed scheme, the SqueezeNet model is used, which is a Convolutional Neural Network (CNN), where the CNN is a subset of deep learning networks. The disease detection is based on the visual characteristics of the black pepper leaves. Thus, the proposed method is an image classification scheme using a trained SqueezeNet that detects whether the pepper leaves are healthy or diseased. The detectio

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Rana, Md Milon, Tajkuruna Akter Tithy, Nefaur Rahman Mamun, and Hridoy Kumar Sharker. "Plant Leaf Diseases Identification in Deep Learning." Computer Science & Engineering: An International Journal 12, no. 5 (2022): 1–13. http://dx.doi.org/10.5121/cseij.2022.12501.

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Crop diseases constitute a big threat to plant existence, but their rapid identification remains difficult in many parts of the planet because of the shortage of the required infrastructure. In computer vision, plant leaf detection made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. employing a public dataset of 4,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to spot one crop species and 4 diseases (or absence thereof). The trained model achieves an accuracy of 97.35% o

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Husna A, Ahamed S, Jannat M, Muhit AA, and Islam MH. "Characterization of Potato Leaf Disease by Digital Image Processing Technique." Journal of Agriculture, Food and Environment 05, no. 02 (2024): 13–18. http://dx.doi.org/10.47440/jafe.2024.5203.

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Potato is asignificantstaple cropin Bangladesh. The productivity of potatoes decreases by factors such as disease, insect infestation, and rapidvariationsin climateconditions. The classification of potato leaf disease shows avitalrole in preventing a damageof product. To identifythe signsof disease immediatelyappearing in plant,it is essentialto use automated detection techniques. If these epidemics are identified at the initialstage and properactivityis selected, the farmerswould not suffer from significant financiallosses. In this study, the classification of diseasesof potato leaf was propo

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