Cardamom Plant Disease Detection Approach Using EfficientNetV2

Sunil, C K; Jaidhar, C. D.; Patil, Nagamma

doi:10.1109/access.2021.3138920

Cited by 108 publications

(24 citation statements)

References 45 publications

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“…To overcome this problem, the study in [23] used U 2 -Net by first producing a mask of the region of interest from the original image. Then, a bitwise operation was applied to the original image and mask produced by U 2 -Net.…”

Section: ) Authors-built Modelsmentioning

confidence: 99%

FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning

et al. 2023

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The Food and Agriculture Organization of the United Nations suggests increasing the food supply by 70% to feed the world population by 2050, although approximately one third of all food is wasted because of plant diseases or disorders. To achieve this goal, researchers have proposed many deep learning models to help farmers detect diseases in their crops as efficiently as possible to avoid yield declines. These models are usually trained on personal or public plant disease datasets such as PlantVillage or PlantDoc. PlantVillage is composed of laboratory images captured under laboratory conditions, with one leaf each and a uniform background. The models trained on this dataset have very low accuracies when running on field images with complex backgrounds and multiple leaves per image. To solve this problem, PlantDoc was built using 2,569 field images downloaded from the Internet and annotated to identify the individual leaves. However, this dataset includes some laboratory images and the absence of plant pathologists during the annotation process may have resulted in misclassification. In this study, FieldPlant is suggested as a dataset that includes 5,170 plant disease images collected directly from plantations. Manual annotation of individual leaves on each image was performed under the supervision of plant pathologists to ensure process quality. This resulted in 8,629 individual annotated leaves across the 27 disease classes. We ran various benchmarks on this dataset to evaluate state-of-the-art classification and object detection models and found that classification tasks on FieldPlant outperformed those on PlantDoc.

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Section: ) Authors-built Modelsmentioning

confidence: 99%

FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning

et al. 2023

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“…The accuracy of training and validation is measured in three tests with MonileNet_V3_Large for transfer learning with and without the Plant district data set compared to VGG-16 and transfer learning. Paper [6] suggests how to diagnose cardamom plant disease using the effective NETV2 model. A complete set of tests was performed to determine the effectiveness of the method and to compare it with other models such as NET which works well with Convolutional Neural networks.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Irrigation for Crop Management Using IoT

Fazil¹

2022

IJMRA

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Agricultural production involves cultivating plants and raising domesticated animals to produce food and feed for humans and other animals. Agriculture or farming as it is also called is a complex activity, and each aspect of it impacts the overall crop production. Farmers need to manage all segments of crop production to achieve success. Farmers make strenuous efforts to produce good quality crops but they face challenging issues of monitoring and maintaining it around the clock. The problems in the agriculture domain largely affect the food production and supply chain. This project includes the proposal of an integrated crop management system to maintain the health of crops by supplying the required amount of water and nutrients to them. One aim is to reduce the amount of water lost to unnecessary evaporation, a concern in the 21st century. As a result, other factors, such as cost, time, and effective care are also benefited. This is why, after the soil preparation and planting are completed, the growth phase of the plant also requires special attention. The paper discusses a process for detecting and solving plant health issues using an intelligent automated system.

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“…But these models are not scalable, thus cannot be used for large scale deployments. To overcome this issue, work in [7] proposes design of EfficientNetV2, that can be used for multiple plant disease types. Similar models are discussed in [8,9,10], which propose use of Deep Convolutional Generative Adversarial Networks (DC GAN), AlexNet, and ensemble Convolutional Neural Networks (eCNN) which enable estimation of high-density features.…”

Section: Literature Reviewmentioning

confidence: 99%

LSCEC: Design of a High-efficiency Leaf-image Segmentation & Classification Model via Ensemble Compute Processes

Bakshi¹,

Shandilya²

2022

Journal of Pharmaceutical Negative Results

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Real-time leaf image segmentation requires consideration of shadow effects, light variations, capture-angle variations, and zoom effects. Designing an efficient segmentation model that is capable of considering these effects is highly complex, and requires context-specific segmentation techniques. Such techniques are computationally complex, and require larger delays, which limits their applicability for real-time use cases. Moreover, such models are highly context-sensitive and require continuous reconfiguration for multiple image types. To overcome these limitations, this text proposes design of a high-efficiency leaf-image segmentation & classification model via ensemble compute processes. The model initially uses saliency maps for background removal, and cascades it with an ensemble of multiple Fuzzy C Means (FCM) segmentation techniques. These include, Enhanced FCM, k FCM, and original FCM, which results in 3 different image sets. Each of these image sets are individually processed to identify green & yellow coloured foreground pixels. The extracted image mask is filled for better segmentation, and resulting image sets are processed for extraction of features. To efficiently represent the images, a set of Colour, Texture & Convolutional feature maps are extracted, which assists in identification of multidomain feature sets. These feature sets are classified by a combination of Naïve Bayes (NB) Multilayer Perceptron (MLP), Logistic Regression (LR), and Support Vector Machine (SVM) based classification engines, which assists in improving efficiency of disease detection for different crop types. Due to this combination, the proposed model is able to achieve 3.5% better classification accuracy, 1.8% faster response, 4.9% higher precision, and 5.5% better recall when compared with state-of-the-art models. Due to which, the proposed model is capable of deployment for a wide variety of real-time use cases.

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Cardamom Plant Disease Detection Approach Using EfficientNetV2

Cited by 108 publications

References 45 publications

FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning

FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning

Smart Irrigation for Crop Management Using IoT

LSCEC: Design of a High-efficiency Leaf-image Segmentation & Classification Model via Ensemble Compute Processes

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