Dataset for leaf disease. From these results, it is clear the complexity of the banana leaf morphology, disease symptoms, multiple classes in single image, field background noises etc. 2000 images of marigold leaves were included in the dataset, with 200 images for each of the ten groups. 01FPS. Feb 21, 2023 · A DL-based CNN model is trained using 2029 images and detects the five apple diseases on leaves. g. Download: Download high-res image (442KB) Download: Download full-size image; Fig 9. The BananaLSD dataset contains 937 images of banana leaves collected Aug 1, 2023 · The dataset includes eight different types of classes containing disease-affected and disease-free cucumber images (Anthracnose, Bacterial Wilt, Belly Rot, Downy Mildew, Pythium Fruit Rot, Gummy Stem Blight, Fresh leaves, and Fresh cucumber) which were collected from the 6th to 30th of May 2022 from real fields with the cooperation of an expert Dec 12, 2023 · The proposed dataset comprises 3076 images categorized into seven classes, including leaves attacked by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. Jan 30, 2023 · The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. 3 shows each pair of leaf diseases in the Rice Plant leaf dataset. used a mango leaf dataset of 8853 images and a ResNet-CNNs (ResNet18, ResNet34 and ResNet50) and the Transfer Learning technique for an automatic detection and identification of four mango leaf diseases named, powdery mildew, anthracnose, golmich and red rust Results show that ResNet50 gives best accuracy (91. It includes 3 completely different datasets to increase diversity. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango Aug 21, 2021 · The tomato disease datasets used in Tian et al. Sep 22, 2022 · Leaf spot disease, which causes 10 − 50% loss in sugar beet yield, causes great damage on the leaves. Feb 1, 2023 · Leaf disease detection is the most recommended process to detect plant infection by recognizing different symptoms of different infections. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango Sep 22, 2023 · In this article, we present the Banana Leaf Spot Diseases (BananaLSD) dataset, an extensive collection of images showcasing three prevalent diseases affecting banana leaves: Sigatoka, Cordana, and Pestalotiopsis. Traditional disease detection and classification methods heavily rely on centralized data collection and analysis, which may lead to privacy concerns and limited access to diverse datasets []. Data Jun 1, 2021 · JMuBEN2 contains two compressed files where the first file contains 16,979 images of Miner while the other contains 18,985 images of healthy leaves. Because this dataset was not maintained and updated Aug 26, 2021 · The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. Jun 1, 2024 · The PlantVillage dataset consists of 54303 healthy and unhealthy leaf images divided into 38 categories by species and disease. Farmers had provided names in their native languages (Gujarati) and we identiï¬ ed and veriï¬ ed English names of those diseases by consulting with Oct 11, 2022 · The dataset is comprised of 3852 corn leaf images divided into four folders each representing one class of four possible disease states, as follows: 513 Cercospora leaf spot, 1192 common rust, 985 northern leaf blight, and 1162 healthy . Abstract: For healthy yields of crops and to maintain food security, it is essential to identify and classify plant diseases. SVMs are also well-suited for real-time applications, a critical factor in agricultural settings where swift disease detection is paramount. 57%. A. There are three classes in this dataset Nov 10, 2023 · The dataset includes several classes of potato leaf diseases caused by fungi, viruses, pests, bacteria, Phytophthora, nematodes, and healthy leaves. 51%, 98. After combining the original tomato leaf images This dataset contains a total of 1821 images of apple plant leaves with disease symptoms, which consist of 622 images of apple rust, 592 images of apple scab, 91 images of multi diseases, and 561 images of healthy leaf images. 97%, 100% Nov 7, 2023 · Below you can see a list of datasets that will be used to sample the additional unknown imagery. This leaf dataset contains a combination of healthy, apple scab, apple rust, and multiple diseases, as shown in the table1. This can be confirmed by the application and development of technologies such as person reidentification ( Zhong et al. Oct 18, 2023 · Overview: The Rice Life Disease Dataset is an extensive collection of data focused on three major diseases that affect rice plants: Bacterial Blight (BB), Brown Spot (BS), and Leaf Smut (LS). This dataset aims to provide a more accurate representation of potato leaf diseases and facilitate advancements in the current research on potato leaf disease Image dataset containing different healthy and unhealthy crop leaves. While other papers have focused on training models to classify these 38 classes, we were interested in testing the robustness of the state-of-the-art models on a particular plant with Apr 6, 2021 · 1. The lack of proper identification of plant disease at the early stages of Aug 1, 2023 · The SoyNet dataset includes various soybean leaf diseases, including Bacterial blight, Rust, Bacterial pustule, brown spot, Downy mildew, frog leaf eye, and Soybean yellow mosaic. Such datasets provide researchers, agronomists, and farmers with a valuable resource to identify, classify, and study various leaf diseases affecting sugarcane crops [2]. Conclusion May 16, 2021 · In total, the dataset contains 58,555 leaf images spread across five classes (Phoma, Cescospora, Rust, Healthy, Miner,) with annotations regarding the state of the leaves and the disease names. Example of leaf images from the PlantVillage dataset, representing every crop-disease pair used. This work presents a carefully curated dataset called "Neem Leaf Disease" consisting of 12880 colour images. Using an open source dataset, PlantVillage, the authors in constructed deep CNN models for plant leaf disease detection. The precession, recall and F1-scores of SVM, KNN and CNN is tabulated in Table 1. The aim of collecting the dataset is to design an automated blackgram plant leaf disease In recent years, many researchers have utilized deep learning methods to detect and identify plant diseases to help farmers improve the quality of their crops. 7. The study utilizes a dataset of over 2000 images of healthy and unhealthy potato leaves, sourced from platforms like Kaggle. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. 256x256 color images of leaves healthy and affected. The data is organized into multiple folders, each corresponding to a different disease affecting tomato leaves. This article introduces Arabica coffee leaf datasets known as JMuBEN and JMuBEN2. The dataset contains 2987 grape leaf images consisting of three categories, i. These disease images are stored in the respective disease folder. The classes: Oct 1, 2023 · The dataset could be useful for plant pathologists as well as producers for detecting disease symptoms. Artificial Intelligence based classification of diseases in maize/corn plants Corn or Maize Leaf Disease Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. org), therefore we get the unaugmented dataset from a paper that used that dataset and republished it. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. It also explores Explainable AI (XAI) to enhance the interpretability of deep learning models’ decisions for end-users. The PlantVillage dataset consists of 54303 healthy and unhealthy leaf images divided into 38 categories by species and disease. It has two versions, an original version and a data augmentation version. This work aimed to determine the significance and perform fine-tuning of state-of-the-art models to detect and classify diseases in plant leaves. Accurate and timely identification of plant diseases is crucial to prevent disease spread and ensure food security 2,3,4. The above diseases are common types of diseases in peanuts Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Jun 11, 2021 · Early identification of crop disease can aid the farmers to take timely precautions and countermeasures for its removal. Feb 1, 2024 · The proposed dataset comprises 3076 images categorized into seven classes, including leaves attacked by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called Nov 4, 2022 · To detect and classify plant leaf diseases which degrades the quality of the black gram crop, in early stages, using computer vision algorithms, a Black gram Plant Leaf Disease (BPLD) dataset was created and briefly discussed in this article. The plants and the leaf diseases under study are listed in Table 2 . com which was collected from orchards in various mango-growing regions of Bangladesh. Mar 19, 2022 · This dataset contains 8,875 healthy and unhealthy leaf images divided into four categories based on species and disease. 65% The dataset contains images for 14 plants along with the leaf diseases typical for each plant, summing to a total of 38 distinct [plant, disease] combinations. This dataset aims to provide a more accurate representation of potato leaf diseases and facilitate advancements in the current research on potato leaf disease Jul 7, 2023 · For crop disease detection, Amreen Abbas et al. In total, the dataset contains 58,555 leaf images spread across five classes (Phoma, Cescospora and Rust Healthy, Miner,) with annotations regarding the state of the leaves and the disease names. Feb 29, 2024 · We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. To show the efficacy of our dataset, we learn 3 models for the task of plant disease classification. This research survey provides a comprehensive understanding of common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and summarizes avail-able datasets. Leaf disease datasets and iCassava 2019 were the two kinds of dataset used. The leaf images are in JPEG format with a size of 256 × 256 pixels. 96%, 99. Oct 18, 2023 · Plant Village Dataset is currently the most widely used and popular public dataset for leaf disease classification. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. (1) Apple Scab, Venturia inaequalis (2) Apple Black Rot, Botryosphaeria obtusa (3) Apple Cedar Rust, Gymnosporangium juniperi-virginianae (4) Apple healthy (5) Blueberry healthy (6) Cherry healthy (7) Cherry Powdery Mildew, Podoshaera clandestine (8) Corn Gray Leaf Spot, Cercospora zeae PlantDoc is a dataset for visual plant disease detection. 3. This dataset comprises images of tomato leaves Jan 31, 2024 · Potato leaf diseases pose significant challenges to agriculture productivity [], affecting crop yield and quality. mendeley. The geographical position of these four gardens is depicted in Fig. A deep convolutional neural network architecture is proposed to classify the crop disease, and a single shot detector is used for Discover datasets around the world! This dataset consists in a collection of shape and texture features extracted from digital images of leaf specimens originating from a total of 40 different plant species. Mar 1, 2024 · The proposed models were evaluated using self-generated database and Kaggle with a total of 2400 images. These diseases caused extensive damage to the Feb 27, 2024 · The paper suggests that image processing is an effective approach for detecting and analyzing potato leaf diseases. Aug 19, 2024 · This dataset consists of images of tomato leaves affected by various diseases. Oct 21, 2021 · The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Farmers had provided names in their native languages (Gujarati) and we identiï¬ ed and veriï¬ ed English names of those diseases by consulting with Oct 3, 2022 · Overfitting in deep learning can be resolved using the data augmentation approach. This disease physiologically appears as individual circular spots on the sugar beet leaves and over time spreads to the entire leaf, resulting in complete death of the leaf. Apple_scab 2. • The dataset could be useful for early leaf spot disease detection and appropriate intervention for banana growers in the context of smart farming. In this paper, a real-time system to identify the type of disease present in a crop based on leaf images using machine learning is proposed. A three step detection model for plant diseases was constructed using healthy and disease leaf images of bell pepper, potato and tomato (Fig. Here goes the list: bacterial_spot Jul 15, 2022 · The Dataset is having five categories of 1000 images (Four most common leaf diseases of Blackgram crop such are Anthracnose, Leaf Crinkle, Powdery Mildew, Yellow Mosaic & healthy category). Apr 1, 2024 · An image dataset specific to sugarcane leaf diseases holds significant importance in the agricultural domain. 5. The techniques are image flipping, Gamma correction, noise injection, PCA color augmentation, rotation, and Scaling. The dataset was used to develop the BananaSqueezeNet model . Nov 6, 2023 · For the first three experimental scenarios, the results presented below refer to the vine leaf dataset, while the results for the fourth scenario correspond to a synthesis of our four datasets. . 28%. As they trained on a small dataset, this reported a classification accuracy of 78. Therefore, in our study, Faster R-CNN, SSD, VGG16, Yolov4 deep learning models were used directly, and Yolov4 deep Feb 9, 2023 · Plant disease severity, defined as the ratio of plant units with visible disease symptoms to the total plant unit (e. Note: The original dataset is not available from the original source (plantvillage. The images are in high resolution JPG format. from publication: Deep Learning-Based Leaf Disease Detection in Crops Using Images for Feb 24, 2021 · Xie et al. After applying augmentation techniques, the total number of images reaches 1186, encompassing a diverse range of situations, including the presence of multiple stresses co-occurring on a The data is for object detection model, which contains images of tomato leaves with 9 diseases and 1 healthy class. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. May 29, 2024 · Though many algorithms have been designed for early diagnosis of plant leaf diseases in existing literature, the bulk of those lack a large enough dataset for accurate detection and diagnosis. e. 88% since 2022. Worldwide crop loss due to plant disease is estimated to be around 14. Oct 5, 2023 · From the literature analysis, we have seen that most of the research papers are applied to the following datasets like Rice, Pepper, Citrus Leaf, Wheat crops for leaf disease detection. [44] first applied this architecture in the field of leaf disease detection for highly imbalanced datasets. The introduction of this new dataset will facilitate a more accurate representation of potato leaf diseases and will allow for the advancement of current research on potato leaf disease identification. Leaf disease detection and categorization employ a variety of deep learning approaches. The data augmentation technique was implemented in an experimental setup that included cut-out, rotation, zoom, shift, brightness, and mix-up. , 89. Dataset for semantic leaf disease segmentation. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. In this research, the aimed to create a convolutional neural network-based deep learning algorithm for accurately classifying various marigold leaf diseases. We have also discussed how Machine-Learning and Deep-Learning based (Black-Box) solutions have aided in disease detection in plant leaves. The dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. 1 Jan 16, 2024 · The dataset includes six prominent categories of black pepper leaf diseases those are Anthracnose, Early stage phytophthora, Slow wilt, Yellowing, Phytophthora and healthy leaves 11. Jul 18, 2020 · The data set contains 5932 number images includes four kinds of Rice leaf diseases i. are generated at South China Agricultural University which consists of 1000 leaf disease images among which 200 images are with white backgrounds and 800 images have a natural background. The images were collected under various conditions and are intended for use in training machine learning models for disease detection and classification. The accuracy of five, seven, and ten classes on the PlantVillage dataset was 99. Nov 30, 2021 · Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. Apr 13, 2019 · The dataset was created by manually separating infected leaves into different disease classes. In this study, five major diseases (as shown in Fig. , 2018 ). By analysing these images, experts can develop more accurate Jul 26, 2023 · The CoSEV dataset consists of 496 filtered images of cotton leaves, captured both in controlled conditions and real-field settings using a smartphone camera. Therefore Jun 1, 2024 · Citation:; @article{rauf2019citrus, title={A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning}, author={Rauf, Hafiz Tayyab and Saleem, Basharat Ali and Lali, M Ikram Ullah and Khan, Muhammad Attique and Sharif, Muhammad and Bukhari, Syed Ahmad Chan}, journal={Data in brief}, volume={26}, pages={104340}, year={2019}, publisher Apr 13, 2019 · The dataset was created by manually separating infected leaves into different disease classes. There have Sep 4, 2022 · The CNN model is implemented to predict the tea leaf disease and achieved excellent accuracy of 100% for training, validation, and test datasets. The datasets include valid, test, and train subdirectories, and the images are Jul 7, 2022 · Rice Leaf Disease Dataset (RLDD) is a small database with only 120 images of infected rice leaves. Aug 28, 2024 · To address the challenges in automated detection and classification of Neem (Azadirachta indica) leaf diseases, having access to a comprehensive and high-quality annotated image dataset is essential. Plant diseases can reduce food production and affect food security. proposed a Faster DR-IACNN model based on the self-built grape leaf disease dataset (GLDD) and Faster R-CNN detection algorithm, the Inception-v1 module, Inception-ResNet-v2 module and SE are introduced. The proposed model produced good results for the leaf disease datasets from PlantVillage data compared with state-of-the-art models with fewer parameters (Table 11). Through accurate identification and classification of cotton leaf diseases, the dataset enables early detection, empowering farmers to take It is particularly useful when dealing with complex datasets containing a variety of leaf diseases. The dataset is collected from the cultivation fields at Nagayalanka, Krishna (d. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. The proposed model achieved higher feature extraction ability, the mAP accuracy was 81. Tobacco is one of the most important crops. Sep 12, 2022 · The leaf disease detection model trained on a large-scale dataset can be directly applied to various practical leaf disease identification tasks without retraining or fine-tuning. Most of the existing methods are based on image classification, which can not identify and locate the specific type and Mar 9, 2024 · The cassava dataset has images of cassava leaves with 4 distinct diseases as well as healthy cassava leaves. Apr 17, 2019 · In this data-set, 39 different classes of plant leaf and background images are available. 9 shows the comparison of proposed model with the existing models. The motivation behind choosing the two dataset is to provide large collection of images and giving the organized data required to train a classification model. The model can predict all of these classes as well as sixth class for "unknown" when the model is not confident in its prediction. One of them is a beans leaf disease dataset, so that the model has exposure to diseased plants other than cassava. K The research mainly focused on diseases appearing on leaves even though it included some non-leaf disease classes such as Cassava root rot (78 images) and Corn charcoal (8 images). Oct 24, 2023 · This mint leaves dataset has the potential to be used to address a variety of research questions in the field of leaf quality assessment such as how machine learning algorithms be used to accurately discern the condition of mint leaves?, can the dataset be used to automate the process of leaf quality assessment?, how can the dataset be used to improve the efficiency and accuracy of decision Download scientific diagram | Sample images from PlantVillage dataset for 38 types of leaf diseases. Dhan-Shomadhan: A datasets of 5 different harmful diseases of rice leaf called Brown Spot, Leaf Scaled, Rice Blast, Rice Turngo, Steath Blight. 75% accuracy on the potato leaf disease dataset. The dataset was downloaded from data. The first file contains 7682 images of Cerscospora, the second contains 8337 Dec 20, 2023 · For example, Gulavnai and al. Aug 12, 2019 · Moreover, this accuracy was further increased (more than 90%) when the new image dataset contain only one focused leaf per image (Additional file 2: Table S4). Image acquisition was done in Mutira coffee plantation in Kirinyaga county-Kenya under real-world conditions using a digital camera and with the help of a pathologist. Feb 26, 2024 · The study considers tomato class with 16,703 plant images obtained from the PlantVillage dataset entailing 1,591 healthy leaves, 373 Mosaic Virus, 3,209 Yellow Leaf Curl Virus, 1,404 Target Spot, 1,676 Spider Mites Two Spotted Spider Mite, 1,771 Septoria leaf spot, 952 Leaf Mold, 1,909 Late Blight, 1,000 Late Blight, 2,127 Bacterial Spots Feb 11, 2020 · Disease dataset and configuration of the system. 1) due to pest and pathogen have been identified. , grape black measles, black rot, and grape healthy. Jun 1, 2022 · The proposed model is tested on tomato leaf disease dataset with an overall 600 samples. 1. These images have been taken from a rice field in India. first used the generation of synthetic pictures of tomato plant leaves against the network C-GAN , then used the tomato disease detection method based on deep learning to migrate learning on DenseNet121. DATASET CROPS AND DISEASES The distribution of Fieldplant images diseases is shown in Fig. Dec 17, 2023 · This paper surveys diverse plant leaf diseases and discusses different leaf disease detection methods and various datasets for plant leaf disease detection. May 12, 2023 · Because the tomato leaf disease detection dataset is the foundation of the study, we chose 5 categories of tomato leaf diseases, the characteristics and numbers of which are shown in Table 1. Nov 6, 2022 · Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Bacterial blight, Blast, Brown Spot and Tungro. 2 Jun 1, 2024 · This dataset consists of 4502 images of healthy and unhealthy plant leaves divided into 22 categories by species and state of health. , 2022), in which a dataset called SLD2K has been built. Apple_cedar_apple Nov 3, 2022 · More clearly, for all the ten classes of tomato plant leaf diseases from the PlantVillage dataset, the ResNet-34-based Faster-RCNN approach acquires the average accuracies of 99. They have observed the classification result of this model by considering AUC and classification accuracy (CA) and it provides maximum values for CA and area under the curve (AUC) i. • The dataset can be used for training, testing and validation of classification algorithms using images of Esca disease and healthy leaves, and to develop computer, smartphone and/or embedded applications addressed to early Sep 1, 2022 · Cassava is a typical staple food in the tropics, and cassava leaf disease can cause massive yield reductions in cassava, resulting in substantial economic losses and a lack of staple foods. leaves), is an important quantitative indicator for many diseases 5. The dataset has been curated to assist researchers, agronomists, and machine learning practitioners in understanding, diagnosing, and potentially predicting the occurrence of these diseases, based on Sep 21, 2016 · Figure 1. Tea leaves were collected from four renowned tea gardens in the Sylhet district of Bangladesh as shown in Fig. The proposed deep learning technique achieved 99. 3 describes Rice Plant leaf image datasets used for disease prediction and collection of Rice Plant leaf different types of leaves of healthy and disease images. JMuBEN dataset contains three compressed folders with images inside. Apple_black_rot 3. Agriculture plays a significant role in meeting food needs and providing food security for the increasingly growing global population, which has increased by 0. Fig. All the leaf has resized the pictures to 256 × 256 pixels and performed model optimization and predictions Apr 1, 2023 · The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. file with label prefix 0001 gets encoded label 0). May 22, 2024 · Plant diseases pose a threat to the global food production 1. 8%. The dataset in this paper is composed mostly of 2 parts, one of which was filtered from PlantVillage . Images of disease infected rice plant leaves Rice Leaf Diseases Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Datasets are the fuel for the development of these technologies. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. Moreover, we dropped images with The model is trained on a dataset of 3600 mango leaf images representing 7 common diseases and healthy leaves. Detecting plant Apr 1, 2021 · Therefore, it enables researchers to perform machine learning methods for early identification of grapevine diseases. The Arabica datasets contain images that facilitates training and validation during the utilization of deep learning algorithms for coffee plant leaf May 5, 2023 · Crop detection and classification using leaf images. 2. SLD2K consists of 2095 real images of six common sugarcane leaf diseases (brown spot, brown stripe, downy mildew, red rot, ring spot and yellow spot) and one class of healthy leaves, as shown in Fig. 19% and 88. May 20, 2024 · The Cotton Leaf Disease Detection Dataset represents a valuable resource for researchers, practitioners, and stakeholders in the agricultural sector, offering insights and tools to address the challenges associated with cotton leaf diseases effectively. Dhan-Shomadhan datasets can use for rice leaf diseases classification, diseases detection using Jun 18, 2024 · The dataset is used to detect four classes of leaves, namely grey blight, brown blight, red rust, and healthy leaf, and aims to provide an effective solution for identifying common tea foliage May 1, 2024 · For this work, 5,702 images were collected from the potato leaf disease dataset and the Plant Village Potato dataset. In this dataset, 200 images with the white color background are exclusively used for experimental purpose and Aug 10, 2022 · Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. New Plant Diseases Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. • The dataset facilitates the low-cost banana leaf spot disease detection model. Apr 13, 2023 · Study area. Citation: Sethy, P. This dataset contains 54,306 images Jun 1, 2024 · Fig. The dataset holds a total of 1000 images belongs to five classes: four diseases and one healthy. t), Andhra Pradesh, India. Sep 1, 2023 · Bhatia et al. Dhan-Shomadhan datasets contains 1106 picture in two different background variation named field background picture and white background picture. After potato and sweet potato, it is the third most widely produced Feb 9, 2022 · The training set and test set were constructed according to the ratio of 4:1 for images of each type of tea leaf disease, that is, 140 images of each type of tea leaf disease were randomly Jun 1, 2024 · When comparing two different image datasets, namely the plant village (PV) dataset, chilli leaves dataset created by Mahaning Hubballi from Kaggle, with a well-established dataset, and a newly collected dataset of chilli and onion leaves images called COLD, the differences and practical implications for image classification are significant. We had consulted the farmers and had asked them to provide names of diseases for sample leaves. There are no files with label prefix 0000, therefore label encoding is shifted by one (e. This dataset aims to provide a more accurate representation of potato leaf diseases and facilitate advancements in the current research on potato leaf disease Nov 10, 2022 · This dataset has 6033 disease images from five categories, namely healthy leaves (HL), rust disease on a single leaf (RD), leaf‐spot disease on a single leaf (LSD), scorch disease on a single leaf (SD), and both rust disease and scorch disease on a single leaf (SD+RD) (Figure 1). Jul 1, 2022 · In classifying 43 different classes of plant leaf dataset, the proposed model achieved an overall accuracy of 99. There are few researches on disease identification using deep learning. However, the existing convolutional neural network (CNN) for cassava leaf disease classification is easily affected by environmental background noise, which makes the CNN unable to extract robust features Apr 1, 2024 · We have done disease recognition of sugarcane leaves in a previous study (Li et al. Caffe DL approach were used where feature maps of infected leaves are used for training purposes; An original Rice Leaf Disease Dataset (RLDD) was constructed from both an online database and their own dataset. Each image is composed of a single leaf and a single background, for a total of 14,531 images. Dec 12, 2023 · The proposed dataset comprises 3076 images categorized into seven classes, including leaves attacked by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. The images were captured using mobile phone cameras under various angles to represent May 27, 2020 · This database is divided into two datasets for tomato leaf images according to different image sources. The tomato leaf images of the first dataset are selected from the PlantVillage database with ten categories (nine disease categories and one health). The classes are, 1. The data-set containing 61,486 images. 1%. To detect and classify the health status of leaves, pre-trained CNN models are employed. , 2017 ) and face recognition ( Liu et al. 50%). The same dataset is also applied to the deep convolutional neural network (DCNN) such as ResNet50, Xception, and NASNetMobile to predict tea leaf disease. 1% and the detection speed was 15. We used six different augmentation techniques for increasing the data-set size. ommyt cgiszd liytez oeob axvo wyem enbz xqutffn czror grea