Automatic method for classification of groundnut diseases using deep convolutional neural network

Vaishnnave, M. P.; Devi, K. Suganya; Ganeshkumar, P.

doi:10.1007/s00500-020-04946-0

Cited by 33 publications

(17 citation statements)

References 30 publications

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“…The initial image samples is presented in L*a*b after the samples get segmented with Kmeans clustering. [86], [87] The textural attributes gain with the help of Local Binary Patterns (LBP), local features extracted using Bag of Words (BoVW) algorithm and Speeded up Robust features (SURF), color features extracted using color moment technique. The final step is classification that is performed through supervised leaning classifier Support Vector Machine (SVM) that obtained the accuracy of 75.8% [89].…”

Section: Legumesmentioning

confidence: 99%

“…The features extraction and classification done using CNN Firstly the background is subtracting to gain only leaf area of these images. Then these images were train on CNN model SoyNet architecture, as compared to existing models the proposed model provide higher F1 score (97), precision (97), recall (97) and accuracy of 98.14% in soybean diseases recognition [92]. Soybean leaf disease present the a verity of characteristics that are local outbreaks and large impact, large variety thus an automate system is required to discover these diseases, to conduct the experiment the 1470 sample images were collected from a farm but these samples were not sufficient and led the model to overfitting.…”

Section: Legumesmentioning

confidence: 99%

“…For this purpose a large dataset of 10 diseased classes collected. The model delivers different accuracies at different steps but overall provide 99.88% accuracy for each class [97]. The summary of legumes (Soybean, Pea, Common Bean and Groundnut) disease detection along with technique and accuracy presented in table 4.…”

Section: Legumesmentioning

confidence: 99%

“…Fig.6. Legumes diseased images (a): frogeye leaf spot; (b): septorial leaf spot; (c): downy mildew; (d): pestalotiopsis; (e): late leaf spot; (f): rust[86],[87] …”

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confidence: 99%

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Detection and Classification of Grain Crops and Legumes Disease: A Survey

Urva¹

2021

STAIQC

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The agriculture plays very important role as it helps to accomplish the need of food among people. The production in agriculture consequentially contributes to the economy of every country. The grain crops rice, wheat, maize, and legumes are suffering a lot due to some viral, bacterial, and fungal diseases. The pest and variety of diseases can bring a heavy loss to the global economy. The monitoring of crops health and identification of diseases at early days is very challenging and emerging task in agriculture. So, it is very important to prevent crops from fatal diseases in the early stage, but the manual process of disease discovery can lead to erroneous magnitude of pesticides. The trouble is figure out by automate discovery of diseases and supplication of relevant medication on time. It is very necessary to find out accurate disease to overcome heavy loss to economy. From the few decades, to detect disease correctly, the process of detection become automate using emerging technologies and techniques using computer vision, machine learning and image processing. This article presents the extensive literature on existing methodologies utilized for recognition and classification of leaves diseases. The studies addresses that there is still many limitations and challenges find in different phases in plant disease detection system. The presented research also highlights the pros and cons of different techniques that help out the researchers for contribution in future.

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Section: Legumesmentioning

confidence: 99%

Section: Legumesmentioning

confidence: 99%

Section: Legumesmentioning

confidence: 99%

“…Fig.6. Legumes diseased images (a): frogeye leaf spot; (b): septorial leaf spot; (c): downy mildew; (d): pestalotiopsis; (e): late leaf spot; (f): rust[86],[87] …”

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confidence: 99%

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Detection and Classification of Grain Crops and Legumes Disease: A Survey

Urva¹

2021

STAIQC

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“…Another CNN-based architecture was proposed to classify disease in the PlantVillage dataset, and it performed better than the well-known DL models including AlexNet, VGG-16, Inception-v3, and ResNet [19]. A recent article proposed a CNN-based model for the classification of groundnut disease [20]. Similarly, few studies focused on the advanced training techniques; for example, [21] evaluated the performance of AlexNet and GoogLeNet trained from scratch and transfer learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Plant Disease Identification by Deep Learning Meta-Architectures

Saleem

Khanchi

Potgieter

et al. 2020

Plants

112

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The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment.

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An Empirical Study of Crop Yield Prediction Using Reinforcement Learning

Vaishnnave¹,

Manivannan²

2022

Artificial Intelligent Techniques for Wireless Communication and Networking

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Automatic method for classification of groundnut diseases using deep convolutional neural network

Cited by 33 publications

References 30 publications

Detection and Classification of Grain Crops and Legumes Disease: A Survey

Detection and Classification of Grain Crops and Legumes Disease: A Survey

Image-Based Plant Disease Identification by Deep Learning Meta-Architectures

An Empirical Study of Crop Yield Prediction Using Reinforcement Learning

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