Plant disease prediction using classification algorithms

<span lang="EN-US">Machine orientation learning had demonstrated that deep learning (DL)-convolutional neural networks (CNNs) were robust image classifiers with significant accuracy. Although to been functional, DL scope classification as tight, well-defined as possible uses a 2-class object, for instance, cats and dogs. The DL classification faced many challenges, e.g., variation factors, the intra-class variation. This nature is presented in every object, its diversity of an object. The label was an exact given name of an intra-class variation object. Unfortunately, not every object had a specific name, in exceptionally high similarity inside the category. This paper explored those problems in flower plants’ taxonomy naming. In supervised learned of DL, image datasets musted labeled with a meaningful word or phrase that humans are familiar with, a taxonomy naming. Labeled with visual feature extraction brought a fully automatic classification. Flower Plumeria L labeling extracted from perspective dimension scale of petal flower which automatically obtained by contour detection, and peaks of blue green red (BGR) histogram channels from bins histogram after object masked. Dataset collected on photography workbench equipped with webcam and ring light. Results showed labels for intra-class variation of Plumeria L in form of dimension-scale and BGR-peaks. The result of this study presented a novelty in building datasets for intra-class variation for the DL classification.</span>

Section: Resultsmentioning

confidence: 99%

Section: Research Methods 21 Methods Of Labelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Labeling of an intra-class variation object in deep learning classification

Santiary

Swardika

Purnama

et al. 2022

“…In [8] the rank widget scores the attributes according to their correlation with the class. Attribute scoring methods that can be used in rank widget are information gain, information gain ratio and gini [9], [10].…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture

Tangwannawit

2022

This research focused on testing with maize, economical crop grown in Phetchabun province, Thailand, by installing a total of 20 sets of internet of things (IoT) devices which consist of soil moisture sensors and temperature and humidity sensors (DHT11). Data science tools such as rapidminer studio was used for data cleansing, data imputation, clustering, and prediction. Next, these data would undergo data cleansing in order to group them to obtain optimization clustering to identify the optimum condition and amount of water required to grow the maize through k-mean technique. From the analysis, the optimization result showed 3 classes and these data were further analyzed through prediction to identify precision. By comparing several algorithms including artificial neural network (ANN), decision tree, naïve bayes, and deep learning, it was found that deep learning algorithm can provide the most accurate result at 99.6% with root mean square error (RMSE)=0.0039. The algorithm obtained was used to write function to control the automated watering system to make sure that the temperature and humidity for growing maize is at appropriate condition. By using the improved watering system, it improved the efficacy of watering system which saves more water by 13.89%

“…It is because classification is a method of using data to develop a new computational model in a certain area [6], [7]. The classification procedure employs a precise technique that differs from model to model, and a high level of accuracy is achieved when the accuracy reaches 100% [8].…”

Section: Introductionmentioning

confidence: 99%

The feature extraction for classifying words on social media with the Naïve Bayes algorithm

Lubis

Nasution

Sitompul

et al. 2022

To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.