Computational Intelligence for Pathological Issues in Precision Agriculture

Plant Pathology is the scientific study of plant diseases, caused by pathogens and environmental conditions (physiological factors). Detection and grading of plant diseases by machine vision is an essential research topic as it may prove useful in monitoring large fields of crops. This can be of great benefit to those users, who have little or no information about the crop they are growing. Also, in some developing countries, farmers may have to go long distances to contact experts to dig up information which is expensive and time consuming. Therefore, looking for a fast, automatic, less expensive, and accurate method to detect plant diseases is of great realistic significance. Such an efficient system can be modeled by integrating the various tools/techniques of information and communication technology (ICT) in agriculture. The objective of the present chapter is to model an intelligent decision support system for detection and grading of plant diseases which encompasses image processing techniques and soft computing/machine learning techniques.

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MACHINE LEARNING TECHNIQUES IN PLANT DISEASE DETECTION AND CLASSIFICATION – A STATE OF THE ART

INMATEH Agricultural Engineering ◽

10.35633/inmateh-65-38 ◽

2021 ◽

pp. 362-372

Author(s):

John Sreya ◽

Leena Rose Arul

Keyword(s):

Machine Learning ◽

Plant Disease ◽

Machine Learning Algorithms ◽

Plant Diseases ◽

Machine Learning Techniques ◽

Small Scale ◽

Disease Detection ◽

Detection Systems ◽

Learning Techniques ◽

Processing Techniques

As we belong to a developing country, the agricultural importance is a known criterion. Majority of the Indians depend on agriculture for their basic living. It also serves as the backbone of the Indian economy. Therefore this sector should be considered important and taken care of. Diseases affecting the plants and pest are the two major threats of agriculture production. Naked eye observation followed by the addition of chemical fertilizers is the traditional method adopted by most of the farmers to avoid plant diseases. But the main limitation to this method is that it works only in the case of small scale farming. In order to tackle this issue many automatic plant disease detection systems have been developed from the early 70s. This paper is intended to survey some of the existing works in plant disease recognition that include various procedures, materials and approaches. They use different machine learning algorithms, image processing techniques and deep learning methods for disease detection. This paper also compares and suggests novel methods to recognize and classify the various kinds of infections affecting agricultural plants.

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UAV technology and machine learning techniques applied to the yield improvement in precision agriculture

2017 IEEE Mexican Humanitarian Technology Conference (MHTC) ◽

10.1109/mhtc.2017.8006410 ◽

2017 ◽

Cited By ~ 5

Author(s):

Jaen Alberto Arroyo ◽

Cecilia Gomez-Castaneda ◽

Elias Ruiz ◽

Enrique Munoz de Cote ◽

Francisco Gavi ◽

...

Keyword(s):

Machine Learning ◽

Precision Agriculture ◽

Machine Learning Techniques ◽

Yield Improvement ◽

Learning Techniques

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Prediction of Neurological Disorders Using Visual Saliency

Advances in Medical Technologies and Clinical Practice - Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease ◽

10.4018/978-1-7998-7188-0.ch001 ◽

2021 ◽

pp. 1-11

Author(s):

Sreelakshmi S. ◽

Anoop V. S.

Keyword(s):

Neurological Disorders ◽

Early Stage ◽

Brain Magnetic Resonance Imaging ◽

Visual Saliency ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Magnetic Resonance Imaging Mri ◽

Processing Techniques ◽

The Brain ◽

Prompt Diagnosis

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

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Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

Journal of The Electrochemical Society ◽

10.1149/2.0222003jes ◽

2020 ◽

Vol 167 (3) ◽

pp. 037522 ◽

Cited By ~ 7

Author(s):

Yemeserach Mekonnen ◽

Srikanth Namuduri ◽

Lamar Burton ◽

Arif Sarwat ◽

Shekhar Bhansali

Keyword(s):

Machine Learning ◽

Wireless Sensor Network ◽

Sensor Network ◽

Precision Agriculture ◽

Machine Learning Techniques ◽

Wireless Sensor ◽

Learning Techniques

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Automated Teller Machine Security with Image Processing and Machine Learning Techniques

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9100 ◽

2020 ◽

Vol 17 (9) ◽

pp. 4473-4481

Author(s):

M. L. Chayadevi ◽

Sujith Madhyastha ◽

K. N. Nisarga ◽

H. Charitha ◽

B. Susharan

Keyword(s):

Image Processing ◽

Machine Learning Techniques ◽

Alert System ◽

Automated Teller Machines ◽

Learning Techniques ◽

Automated Teller Machine ◽

Haar Cascade ◽

Physically Challenged ◽

Processing Techniques ◽

Day By Day

There are many scenarios in society with thefts and crimes involved in Automated Teller Machines (ATM). These events are increasing day-by-day and which is also increasing the complexities on the crime investigation agencies. In order to deal with these situations, we have proposed an automated security method inside ATMs using image processing techniques which can alert the concerned authorities immediately whenever these types of situations arise. Hybrid method with Viola-Jones algorithm has been used for face recognition along with the Haar-cascade features. In the case of objects such as knife, gun etc. inside an ATM, combination of SVM and random forest algorithms are used for object detection. TensorFlow with machines learning algorithms have been used in the hybrid methodology. Android application has been developed to prevent and alert the crimes and send alert messages to the concerned. Speech alert system is developed to assist blind and physically challenged people.

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Identification of Plant Leaf Disease using Machine Learning Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5621.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 6077-6081 ◽

Cited By ~ 1

Keyword(s):

Plant Disease ◽

Digital Camera ◽

Machine Learning Techniques ◽

Support Vector ◽

Leaf Sample ◽

Learning Techniques ◽

Artificial Neural Network Ann ◽

Identification Techniques ◽

Processing Techniques ◽

Better Than

Plant disease identification and classification is major area of research as majority of people in India depend on agriculture for their main source of income and for food. Identification of the diseases in any crops is challenging since manual identification techniques being used in this are based on the experts advises which may not be efficient. Based on leaf features decisions about variety of diseases are taken. In this paper an automated framework is introduced which can be used to detect and classify the diseases in the leaf accurately. Leaf images are acquired by using digital camera. Pre-processing techniques, segmentation and feature extraction are performed on the acquired images. The features are passed on to the classifiers to classify the diseases. This work has been proposed to classify and distinguish the leaf sample based on its features. The proposed work is carried out with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes classifiers to analyze the result. For given dataset ANN performed better than the other two classifiers

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Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

10.20944/preprints202111.0153.v1 ◽

2021 ◽

Author(s):

Mehmet Akif Cifci

Keyword(s):

Machine Learning ◽

Diabetic Retinopathy ◽

Early Detection ◽

The Other ◽

Machine Learning Techniques ◽

Image Processing Techniques ◽

Learning Techniques ◽

Fundus Photographs ◽

Processing Techniques ◽

Time And Energy

The complication of people with diabetes causes an illness known as Diabetic Retinopathy (DR). It is very widespread among middle-aged and older people. As diabetes progresses, patients' vision may deteriorate and cause DR. People to lose their vision because of this illness. To cope with DR, early detection is needed. Patients will have to be checked by doctors regularly, which is a waste of time and energy. DR can be divided into two groups: non-proliferative (NPDR) while the other is proliferative (PDR). In this study, machine learning (ML) techniques are used to diagnose DR early. These are PNN, SVM, Bayesian Classification, and K-Means Clustering. These techniques will be evaluated and compared with each other to choose the best methodology. A total of 300 fundus photographs are processed for training and testing. The features are extracted from these raw images using image processing techniques. After an experiment, it is concluded that PNN has an accuracy of about 89%, Bayes Classifications 94%, SVM 97%, and K-Means Clustering 87%. The preliminary results prove that SVM is the best technique for early detection of DR.

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DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS

ECUADOR ES CALIDAD: Revista Científica Ecuatoriana ◽

10.36331/revista.v2i1.5 ◽

2015 ◽

Vol 2 (1) ◽

Author(s):

Victor Rueda Ayala ◽

Seshadri Kunapuli ◽

Javier Maiguashca

Keyword(s):

Precision Agriculture ◽

Prediction Models ◽

Polynomial Regression ◽

Machine Learning Techniques ◽

Yield Prediction ◽

Multinomial Regression ◽

Maize Crop ◽

Learning Techniques ◽

Predictive Algorithms ◽

National Production

Machine learning techniques were applied with statistical tools such as linear, logistic and multinomial regression, to work out predictive algorithms for yield estimation. Spectroradiometer readings were collected throughout the main maiz producing provinces of Ecuador, at two crop development stages.A model using six degree polynomial regression is recommended for acceptable yield prediction. This model could contribute to decide about imports strategies and avoid the overlapping with the national production.

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Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images

Machine Learning ◽

10.4018/978-1-60960-818-7.ch406 ◽

2012 ◽

pp. 817-829

Author(s):

Nikolaos Giannakeas ◽

Dimitrios I. Fotiadis

Keyword(s):

Machine Learning ◽

Image Processing ◽

Expression Profiles ◽

Machine Learning Techniques ◽

Experimental Conditions ◽

Microarray Image ◽

Learning Techniques ◽

Common Substrate ◽

Processing Techniques ◽

Comprehensive Measurement

Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical applications of microarrays include the quantification of expression profiles of a system under different experimental conditions, or expression profile comparisons of two systems for one or more conditions. Microarray image analysis is a crucial step in the analysis of microarray data. In this chapter an extensive overview of the segmentation of the microarray image is presented. Methods already presented in the literature are classified into two main categories:methods which are based on image processing techniques and those which are based on Machine learning techniques. A novel classification-based application for the segmentation is also presented to demonstrate efficiency.

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