A Systematic Review of Different Categories of Plant Disease Detection Using Deep Learning-Based Approaches

Kumar, Yogesh; Singh, Rupinder; Moudgil, Manu Raj; Kamini,

doi:10.1007/s11831-023-09958-1

Cited by 17 publications

(7 citation statements)

References 26 publications

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“…The results obtained in our experiments differ from those reported in [6] where, after applying ten deep learning models such as DenseNet201, DenseNet121 [25] (a densely connected convolutional network (DenseNet) is a feedforward architecture in which each layer is linked to every other layer. This allows the network to learn more effectively by reusing features, hence reducing the number of parameters and enhancing the gradient flow during training.…”

Section: Discussioncontrasting

confidence: 99%

“…Particularly, potato (Solanum tuberosum L.) crops are constantly affected by the incidence of parasites which cause a decrease in their yield every year. Being a widespread crop in the world, the control of its production requires attention, and the problem of automatic disease recognition from leaf images via CNNs has been the subject of much recent literature, such as [2][3][4][5][6][7][8], to cite only some contributions. For instance, in [9], potato tuber diseases were diagnosed using the VGG architecture, by adding new dropout layers to avoid overfitting.…”

Section: Introductionmentioning

confidence: 99%

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A Mobile App for Detecting Potato Crop Diseases

Pineda Medina,

Miranda Cabrera,

de la Cruz

et al. 2024

J. Imaging

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Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum tuberorsum L.) crops. In this paper, we present a mobile application for detecting potato crop diseases based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes (healthy, early blight-diseased, late blight-diseased). An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.7%, with MobileNetv2. Based on the results obtained, an offline mobile application was developed, supported on devices with Android 4.1 or later, also featuring an information section on the 27 diseases affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of diseases affecting the same plant.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Mobile App for Detecting Potato Crop Diseases

Pineda Medina,

Miranda Cabrera,

de la Cruz

et al. 2024

J. Imaging

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“…In addition to this, it is a known fact that agriculture systems have become complex at a large scale which requires the urgency towards the reliable https://www.indjst.org/ as well as efficient detection method. Hence, in light of these pressing concerns, there is a critical need to harness emerging technologies and innovative approaches to bolster plant disease management efforts (3,4) . Among these, the application of AI based learning techniques holds promise for revolutionizing disease detection in agriculture.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Plant Disease Detection using Advanced Deep Learning Models

Kaur,

Bansal

2024

IJST

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Objective: The aim of this paper is to analyze the efficacy of employing multiple advanced convolutional neural networks (CNNs) for the purpose of enhancing the accuracy in detecting and classifying various plant diseases. Methods: The research involves the analysis of 7623 training images as well as 1906 validation images of different plant diseases and employed advanced deep learning models like DenseNet169, Xception, InceptionV3, MobileNetV2, and ResNet50V2 to classify them. At first the RGB images are converted to Grayscale and later to enhance their quality, few techniques have been used such as Otsu thresholding, noise removal, distance transform, and watershed techniques. Subsequently, contour features are extracted by calculating morphological values to obtain the necessary region that correspond to diseased areas in plant images. Findings: On evaluating the performance of the applied models on the basis of various metrics, MobileNetV2 and ResNetV2 achieved the highest validation accuracy scores of 99.42% each, with their respective loss values of 0.19 and 0.49. In terms of recall, precision, and F1 score, all models, except MobileNetV2 and InceptionV3, attained optimal scores of 0.99 each. Novelty: The novelty of this paper resides in the incorporation of multiple image segmentation techniques with fine tuning the parameters of advanced Convolutional Neural Network (CNN) models on the basis of various factors such as the number of images, size, channel, classes etc to generate the optimal results. Keywords: Agriculture, Plant diseases, Artificial Intelligence, Advanced CNN models, Watershed Technique

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“…Historically, diagnosing and treating these disorders has been a time-consuming and expensive process that frequently relied on manual examinations by qualified professionals [10]. But with the advent of the digital era, agriculture has undergone a fundamental change [11].…”

Section: Introductionmentioning

confidence: 99%

A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI)

Debnath,

Hasan,

Raihan

et al. 2023

Sensors

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The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2’s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

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A Systematic Review of Different Categories of Plant Disease Detection Using Deep Learning-Based Approaches

Cited by 17 publications

References 26 publications

A Mobile App for Detecting Potato Crop Diseases

A Mobile App for Detecting Potato Crop Diseases

Enhancing Plant Disease Detection using Advanced Deep Learning Models

A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI)

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