A GA_FFNN algorithm applied for classification in diseased plant leaf system

Muthukannan, K.; Latha, P.

doi:10.1007/s11042-018-5710-5

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…There was an activation function that was used in its proposed ANN which is a sigmoid function. Thus, for the hidden layer, output y h for node h, is shown as in (4) (6) Wherein k  denotes the bias of the node k found in its output layer:…”

Section: B Proposed Gso-neural Networkmentioning

confidence: 99%

“…Wherein the i kp y will be its kth computed output as per n equation (6) for ANN that is for the pth sample vector and this is of the ith member; P now denotes the sample vectors; and…”

Section: Fig 1 Schematic Diagram Of Gso-based Annmentioning

confidence: 99%

“…But the classification of the plant leaves has remained a major challenge as there are several factors like leaf pattern complexity, data of the chosen image acquisition and the processing of images there can be some effect on the classification and its success. Even though there has been some research made earlier by the techniques of optimization such as Simulated Annealing (SA), Random Search (RS), Evolution Strategies (ES), Evolutionary Programming (EP) and Genetic Algorithms (GA), there is a comprehensive review of the new techniques made recently and this is valuable to a great extent in choosing a suitable procedure of classification for the diseased plant leaf [6].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Group Search Optimizer for Plant Leaf Classification

Ravishankkar¹,

santhosh²,

Amudhavalli³

2019

IJITEE

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The analysis of Big Data is a data mining discipline in which large quantity of unstructured data is analysed which can be challenging to store and also to retrieve efficiently. The classification of plants is based on the identification of leaf that has a very broad application in both agriculture and medicine. In this work, a method which is computerized is used to recognize a plant leaf based on images is proposed. The proposed method extracts features from the image and these are used for classifying the plant leaf. The process of deciding on the subset for all relevant features to be used in the construction of a system is known as feature selection. The Group Search Optimizer (GSO) is a nature-inspired algorithm that possesses all the qualities used effectively to solve feature selection tasks. In this work, there is a GSO-based algorithm of feature selection along with fuzzy logic and the classifier of a Neural Network (NN) is proposed. The results of the experiment prove the proposed method (GSO-NN) was able to achieve a better level of performance compared to the other methods.

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Section: B Proposed Gso-neural Networkmentioning

confidence: 99%

“…Wherein the i kp y will be its kth computed output as per n equation (6) for ANN that is for the pth sample vector and this is of the ith member; P now denotes the sample vectors; and…”

Section: Fig 1 Schematic Diagram Of Gso-based Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Group Search Optimizer for Plant Leaf Classification

Ravishankkar¹,

santhosh²,

Amudhavalli³

2019

IJITEE

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“…As a result, cutting-edge image processing, machine learning, and deep learning methods are employed. To increase revenues, agricultural products need precise quality control (Muthukannan & Latha, 2018;Murakami et al, 2005;Paulus et al, 1997), and diagnosis and identi cation of plant pests (Abbasgholipour et al, 2011;Boissard et al, 2008;Shariff et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Advancements in Deep Learning for Accurate Classification of Grape Leaves and Diagnosis of Grape Diseases

Kunduracıoğlu¹,

Pacal²

2023

Preprint

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Plant diseases are a major factor contributing to agricultural production losses, necessitating effective disease detection and classification methods. Traditional manual approaches heavily rely on expert knowledge, which can introduce biases. However, advancements in computing and image processing have opened up possibilities for leveraging these technologies to assist non-experts in managing plant diseases. Particularly, deep learning techniques have shown remarkable success in assessing and classifying plant health based on digital images. This paper focuses on fine-tuning state-of-the-art pre-trained convolutional neural network (CNN) models and vision transformer models for the detection and diagnosis of grape leaves and diseases using digital images.The experiments were conducted using two datasets: PlantVillage, which encompasses four classes of grape diseases (Black Rot, Leaf Blight, Healthy, and Esca leaves), and Grapevine, which includes five classes for leaf recognition (Ak, Alaidris, Buzgulu, Dimnit, and Nazli). The results of the experiments, involving a total of 14 models based on six well-known CNN architectures and 17 models based on five widely recognized vision transformer architectures, demonstrated the capability of deep learning techniques in accurately distinguishing between grape diseases and recognizing grape leaves. Notably, four CNN models and four vision transformer models achieved 100% accuracy on the test data from the PlantVillage dataset, while one CNN model and one vision transformer model achieved 100% accuracy on the Grapevine dataset. Among the models tested, the Swinv2-Base model stood out by achieving 100% accuracy on both the PlantVillage and Grapevine datasets. The proposed deep learning-based approach is believed to have the potential to enhance crop productivity through early detection of grape diseases. Additionally, it is expected to offer a fresh perspective to the agricultural sector by providing insights into the characterization of various grape varieties.

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“…India is the major contributor and trader of tomatoes and is in 3rd place for harvesting them around the globe. The leaf of tomato crop experiences a variety of infections, and from the point of research mostly yellow leaf curl, Spider Mites, bacterial leaf mold, leaf spot, Septoria leaf spot (1), tomato mosaic (2), late blight, and early blight(1), (3), (4). Plant infections have transformed into a major issue as they can cause a huge downfall in both the amount of agrarian produce & the quality.…”

Section: Introductionmentioning

confidence: 99%

Tomato Plant Leaf Malady Detection Using Machine Intelligence

Thanvi¹,

Jangir²,

Sharma³

et al. 2022

ECS Trans.

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Tomato crops are progressively mainstream, as a result of their high nutrition power which is available such as beta-carotene and Vitamin C, E. Lack of care for such crops results in causing serious ailments on plants and it is very important for identifying disease in plants for an efficient crop yield. Maladies on the plant causes depletion in both the quantity and quality of the crop. This paper possesses a real-time, precise, and efficient approach based on machine learning to recognize maladies existing in a tomato plant by revealing from its leaves. The model for disease detection was trained on plant leaves which were taken from PlantVillage Dataset and a convolution neural network-based algorithm was used for the identification of maladies in tomato plant leaf. The outcome shows that the presented approach is effective in detecting disease of tomato plant leaves as well as it could be globalized.

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A GA_FFNN algorithm applied for classification in diseased plant leaf system

Cited by 13 publications

References 11 publications

Hybrid Group Search Optimizer for Plant Leaf Classification

Hybrid Group Search Optimizer for Plant Leaf Classification

Advancements in Deep Learning for Accurate Classification of Grape Leaves and Diagnosis of Grape Diseases

Tomato Plant Leaf Malady Detection Using Machine Intelligence

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