Citrus Diseases Recognition by Using CNN

Jasim, Wala'a Nsaif; Almola, Sahera Abued Sead; Alabiech, Mohammed H. Haloob; Harfash, Esraa Jasem

doi:10.31449/inf.v46i7.4284

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Milioris et al [18] investigated and implemented a technique to assess health professionals' views on the adoption and value of health information systems and to assess their usage. Jasim et al [19] implemented CNN based model for building a system to recognize diseases that are happened in citrus.…”

Section: Related Workmentioning

confidence: 99%

Prediction of heart disease using Modified Hybrid Classifier

Pipalwa¹,

Paul²,

Mukherjee³

2023

IJCAI

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This paper proposes a Machine Learning or ML-based strategy to accurately identify a possible heart disease patient. Unlike traditional diagnostic systems which are time-consuming and have human error involved to take care of the patient and diagnose the patients. The proposed system identifies whether the patient will face these kinds of diseases in near future or not. The system is developed based on machine learning techniques such as Naive Bayes, XGBoost gradient classifier, support vector machine, and decision tree. Some external factors were also considered which may lead to heart disease in the future. Furthermore, an integrated web application has been developed that alert and gives a user-friendly interface for recognition and prediction. Thirteen diagnostic factors and five environmental factors are analyzed. The proposed diagnosis system attained good precision as compared to previous methods recommended earlier. In addition, the system can easily be implemented in the public domain to spread awareness regarding heart disease, and it also talks about the possibility of heart disease in near future.Povzetek: Predstavljeni sistem zazna morebitno srčno bolezen iz trinajstih diagnostičnih in petih okoljskih dejavnikov z uporabo algoritmov strojnega učenja.

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Section: Related Workmentioning

confidence: 99%

Prediction of heart disease using Modified Hybrid Classifier

Pipalwa¹,

Paul²,

Mukherjee³

2023

IJCAI

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“…The development of technology bring up the term smart farming which integrates information and communications technology with traditional agriculture [7]. Technology has penetrated into various fields including agriculture, for example the existence of the Internet of Things [4] [8], big data analysis, remote sensing [9], machine learning [10] and unmanned Aerial Vehicle (UAV) [11][12] [13]. The use of Unmanned Aerials, also called as drones, in agriculture can reduce observation time and improve efficiency [14].…”

Section: Introductionmentioning

confidence: 99%

Approach and Analysis of Yolov4 Algorithm for Rice Diseases Detection at Different Drone Image Acquisition Distances

2023

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Rice production plays an important role in people's lives globally because rice is the most widely consumed staple food for over half of the world's human population. Unfortunately, the rice plants are prone to pests and diseases which may result in a decrease in the rice production. Thus, early and accurate detection of rice diseases is needed. This paper discusses a method for detecting rice diseases called You Only Look Once (YOLO) object detection algorithm version 4. A drone camera was used to acquire the images from four different distances, namely 2 meters, 5 meters, 10 meters and 20 meters. This approach aims to detect the presence of pests to be faster, more accurate and more precise. The precision results at each image capture from the different distances were 46.8%, 48%, 65% and 77.3% with the average loss value of 6.52, 0.54, 1.16 and 2.73

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