Tomato leaves diseases detection approach based on Support Vector Machines

Mokhtar, Usama; Ali, Mona A. S.; Hassenian, Aboul Ella; Hefny, Hesham A.

doi:10.1109/icenco.2015.7416356

Cited by 126 publications

(34 citation statements)

References 14 publications

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“…It transforms data by using a method identified as the kernel trick, and then discovers an ideal boundary between the possible outputs based on these transformations. Simply put, it performs some extremely difficult data transformations before determining how to distinct data based on the outputs set or labels [36][37][38].…”

Section: B-naïve Bayesmentioning

confidence: 99%

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

Hassan

Abdulazeez

2021

AJRCoS

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One of the main factors that assist to increase the growth of any country is Agriculture. The detection of diseases from plant leaf images is one of the most important fields of agricultural research. To identify disease factors, this field requires a reliable prediction approach. Data Mining (DM) is the process of analyzing data from different aspects and summarizing it into valuable information. It helps users to categorize and identify relationships between data from various dimensions. As there are many plants on the farm, detecting and classifying the diseases of each plant on the farm is extremely difficult for the human eye. And diagnosing each plant is very critical since these diseases may spread. DM classification is an important method that has a wide range of applications. It classifies each item in a set of data into one of a set of predefined classes. In this paper, a comparison of different DM classification methods such as Naive Bayes, Decision trees, SVM, and Random Forest algorithms has been illustrated by using of Weka Tool.

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Section: B-naïve Bayesmentioning

confidence: 99%

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

Hassan

Abdulazeez

2021

AJRCoS

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“…The constant weight estimation on different kernels has displayed the highest rate of RMSE errors. Pomegranate leaves were classified using SVM classifiers [20] by improvising the segmentation process. Here, statistical features were collected using k-mean clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

An LSTM Based CNN Model for Pomegranate Fruit Classification With Weight Optimization Using Dragonfly Technique

Vasumathi¹,

Kamarasan²

2021

INDJCSE

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Pomegranate is a widely grown plant in India. This highly beneficial fruit is infected by multiple pests and diseases which cause great economical losses. Different forms of pathogen diseases on leaf, stem and the fruits are present. Some of the diseases that affect pomegranate fruits are anthracnose, cercospora, heart rot and bacterial blight. There is a need for disease control strategies to incorporate timely action on the developed diseases. Thus, there is a need for intelligent and self-learning recognition systems to detect these diseases on time. This study is aiming to classify pomegranate fruits into two classes normal and abnormal using CNN LSTM technique. This research work uses a hybrid CNN-LSTM technique to detect four types of diseases present in the pomegranate fruits and classify them into four classes. The results obtained using CNN LSTM are then optimized using dragonfly algorithm. The features like colour, texture and shape of the fruits are collected and fed into the hybrid CNN-LSTM. The dataset for the classifier is given as an excel file which is initially pre-processed using map reduce technique and dimensionality reduction carried using Principal Component Analysis and Discriminant analysis. The CNN LSTM classifier identifies the 4 types of diseases and normal fruit. The classification is further optimized using dragonfly algorithm. The optimized weight and cost function has further explored to support the multi-class disease detection process. Experimental results have shown an accuracy of 92% in classification using CNN-LSTM technique and optimization using dragonfly techniques shows an improved classification accuracy of 97.1%.

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“…ANN arrangement execution is 80% better in precision. The paper [5] discusses an approach that utilizes the method called as Gabor wavelet transformation. It extracts the feature that assists to detect the disease of leaves in tomato.…”

Section: Literature Reviewmentioning

confidence: 99%

Senet Cnn Based Tomato Leaf Disease Detection

Pragy¹,

Sharma²,

Sharma³

2019

IJITEE

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Plants play important roles in the environment. Various plants are fulfilling many demands and basic requirements of the society. Saving such entities in the society is the uttermost necessity in today’s world to deal with plant degradations. Many diseases in plants are common and thus they face degradation. While mainly dealing with common plant such as tomato and potato plants, it is observed that they very often face bacterial and other diseases. A proper precaution can be made to save plant from such diseases. Thus the early prediction of such different diseases can be made, which can be a massive savings to the farmer as well as for the country economy. This paper has adapted a moderate different approach of convolutional neural network called SENet. In this approach, a hybrid process is discussed which uses the advantage of SENet and CNN layer concept for better classification. CNN is performed by using the number of layer and kernel selections. Classification of data is performed using the traditional CNN approach. In this scenario, the quick process occurrence is performed using suppression of less used information. It tries to add weight to each and every feature map in the layer. This approach is used to check, identify and detect the defects in leaf of tomato. The prime motive of the presented approach is; to obtain simple easiest method for the detection of disease in tomato leaf with use of minimal computing resources. Thus an improved, efficient algorithm can be made use in real time implementation of leaf disease prediction with high accuracy and efficiency parameters.

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Tomato leaves diseases detection approach based on Support Vector Machines

Cited by 126 publications

References 14 publications

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

An LSTM Based CNN Model for Pomegranate Fruit Classification With Weight Optimization Using Dragonfly Technique

Senet Cnn Based Tomato Leaf Disease Detection

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