Identification of Paddy Leaf Diseases using Evolutionary and Machine Learning Methods

Patil, Nilam

doi:10.17762/turcomat.v12i2.1503

Cited by 12 publications

(2 citation statements)

References 12 publications

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“…Authors in [73] have proposed an automated model using machine learning and evolutionary approaches to identify paddy leaves diseases. The model can be used on android platform for quick identification of diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and Classification of Plant Leaf Diseases Using Digtal Image Processing Methods: A Review

Mohammed

Yusoff²

2023

AEJ

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Prediction and classification of plant leaf illnesses by farmers using conventional approaches can be unexciting and erroneous. Problems may occur while trying to predict the sort of illnesses manually. Inability to detect diseases of plants promptly could lead to the destruction of crop plants and this can cause serious decline in the yield. Losses can be prevented and yield be maximized when farmers adopt computerized image processing approaches in their farms. Numerous techniques have been proposed and used in the prediction of diseases of crop plants based on the images of the infected leaves. Researchers have in the past achieved a lot in the aspect of plant illnesses identification by exploring several techniques and models. However, improvement needs to be provided on account of reviews, new advancements and discussions. Deploying technology can greatly enhance crop production across the globe. Different approaches and models can be trained with huge data to identify new improved methods for uncovering diseases of plants to tackle problem of low yield. Previous works have determined the robustness of various image processing techniques such as; K-means clustering, Naive Bayes, Feed forward neutral network (FFNN), Support Vector Machine (SVM), K-nearest neighbor (KNN) classifier, Fuzzy logic, Genetic Algorithm (GA), Artificial Neural Network (ANN), Convolutional Neural Network (CNN) etc. This paper provides a critical review and results of different types of approaches and methods used previously to detect and classify various types of plant leaf illnesses using image processing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Detection and Classification of Plant Leaf Diseases Using Digtal Image Processing Methods: A Review

Mohammed

Yusoff²

2023

AEJ

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“…Nilam et al, [1] Paddy leaf illnesses were identified utilising evolutionary and machine learning methods including AdaBoost and Bagging Classifier, as well as evolutionary algorithms like Genetic algorithm. In this proposed system they can obtained the accuracy by GA classifier is 96% at training feature-set and 91% at testing feature-set.…”

Section: Related Workmentioning

confidence: 99%

Paddy Leaf Symptom-based Disease Classification Using Deep CNN with ResNet-50

Rani,

Singh

2022

Int. J. of Adv. Sci. Comp. and Eng.

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Agriculture is a globally important occupation. Food is a fundamental need for all living things on the earth, hence it plays a significant role. As a result, agricultural product quality has to be improved. Paddy is susceptible to a variety of illnesses, just like all other crops. Diseases vary by region and season. Despite the fact that the number of new technologies being implemented in agriculture is rapidly expanding, farmers in our nation continue to rely on old methods for disease detection. Machine learning relies heavily on features to classify images. The advancement of the deep convolutional neural network paves the path for disease detection in rice based on deep characteristics, with the expectation of excellent yields. Field photos of four forms of rice leaf diseases, including bacterial leaf blight, brown spot, leaf smut, and tungro, were introduced using this proposed method. The model is trained using the deep CNN classification technique. The pre-trained ResNet-50 model is also included to increase the model's prediction accuracy. Existing approaches are outperformed by the combination of Deep CNN and ResNet-50. Accuracy was used as a criterion for evaluating performance.

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An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

Neggaz

Fizazi

2022

Soft Comput

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Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person’s gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory’s physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper’s primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher’s uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher’s dataset.

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Identification of Paddy Leaf Diseases using Evolutionary and Machine Learning Methods

Cited by 12 publications

References 12 publications

Detection and Classification of Plant Leaf Diseases Using Digtal Image Processing Methods: A Review

Detection and Classification of Plant Leaf Diseases Using Digtal Image Processing Methods: A Review

Paddy Leaf Symptom-based Disease Classification Using Deep CNN with ResNet-50

An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

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