Automated Irrigation System Using a Wireless Sensor Network and GPRS Module

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

https://www.mdpi.com/1424-8220/20/11/3113/pdf

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

The Master of Fine Arts is a terminal degree in studio art. Our selective and limited enrollment ensures regular individual contact with dedicated, diverse faculty who are committed to a sustained professional exchange with each student. A collaboratively designed curriculum is augmented by regular critiques engaging all studio majors and faculty. Media experimentation is encouraged. Students must be able to apply and communicate a diverse body of knowledge of historical, cultural, contemporary, and aesthetic issues to their professional practice. Students are expected to articulate and defend their position within the context of contemporary art discourse.

磁性 Ceramics의 機能

Detection and classification of rice plant diseases

In this paper, we address a comprehensive study on disease recognition and classification of plant leafs using image processing methods. The traditional manual visual quality inspection cannot be defined systematically as this method is unpredictable and inconsistent. Moreover, it involves a remarkable amount of expertise in the field of plant disease diagnostics (phytopathology) in addition to the disproportionate processing times. Hence, image processing has been applied for the recognition of plant diseases. The paper has been divided into two main categories viz. detection and classification of leafs. A comprehensive discussion on the diseases detection and classification performance is presented based on analysis of previously proposed state of art techniques particularly from 1997 to 2016. Finally, discussed and classify the challenges and some prospects for future improvements in this space.

Study of digital image processing techniques for leaf disease detection and classification

Establishing an accurate count and classification of leukocytes commonly known as WBC (white blood cells) is crucial in the assessment and detection of illness of an individual, which involves complications on the immune system that leads to various types of diseases including infections, anemia, leukemia, cancer, AIDS (Acquired Immune Deficiency Syndrome) etc. The two widely used methods to count WBC is with the use of hematology analyzer and manual counting. Currently, in the age of modernization there has been numerous research in the field of image processing incorporated with various segmentation and classification techniques to be able to generate alternatives for WBC classification and counting. However, the accuracy of these existing methods could still be improved. Thus, in this paper we proposed a new method that could segment various types of WBCs: monocytes, lymphocytes, eosinophils, basophils, and neutrophils from a microscopic blood image using HSV (Hue, Saturation, Value) saturation component with blob analysis for segmentation and incorporate CNN (Convolutional Neural Network) for counting which in turn generates more accurate results.

White blood cell classification and counting using convolutional neural network

In this study, digital image processing was incorporated to eliminate the Subjectiveness of manual inspection of diseases in rice plant and accurately identify the three common diseases to Philippine's farmlands: (1) Bacterial leaf blight, (2) Brown spot, and (3) Rice blast. The image processing section was built using MATLAB functions and it comprises techniques such as image enhancement, image segmentation, and feature extraction, where four features are extracted to analyze the disease: (1) fraction covered by the disease on the leaf; (2) mean values for the R, G, and B of the disease; (3) standard deviation of the R, G, and B of the disease and; (4) mean values of the H, S and V of the disease. The Backpropagation Neural Network was used in this project to enhance the accuracy and performance of the image processing. The database of the network involved 134 images of diseases and 70% of these were used for training the network, 15% for validation and 15% for testing. After the processing, the program will give the corresponding strategic options to consider with the disease detected. Overall, the program was proven 100% accurate.

Identification of diseases in rice plant (oryza sativa) using back propagation Artificial Neural Network

The study was about creating a device that can use colorimetry to determine the Nitrogen, Phosphorus and Potassium content and pH of the soil to be cultivated by using the chemicals of the Soil Test Kit in giving nutrient recommendations. Trials were done to produce the actual color reaction of the reactants which served as the color references. The RGB values were extracted using TCS3471 color sensor and an LDR color sensor. The device was tested using three samples each for every nutrient and pH with ten trials each. The benchmark value used was 0.05 to have a 95% level of confidence. The computed value of Chi-Square was equal to 0 using the table for the critical values 0.05 corresponds to the critical value of 3.841. Therefore, computed value was less than the critical value which can be concluded that there was no significant difference between the proposed device reading the soil pH and nutrient content and the human resource reading.

Soil pH and nutrient (Nitrogen, Phosphorus and Potassium) analyzer using colorimetry

One of the biggest environmental problems right now is air pollution. Air quality is needed to be consistently monitored and assessed to ensure better living conditions. The U.S. Environmental Protection Agency (EPA) uses the air quality index (AQI) to standardize the air quality. However, AQI requires precise and accurate sensor readings and complex calculations, making it not feasible for portable air quality monitoring devices. The aim of this paper is to find an alternative way of monitoring and characterizing air quality through the use of integrated gas sensors and building predictive models using machine learning algorithms that can be used to obtain data-driven solutions to mitigate the risk of air pollution. The proposed methodology is implemented by building a prototype for the integrated sensors using DHT 11 temperature and relative humidity sensor, MQ2, MQ5 and MQ135 gas sensors. Five predictive models are developed in the study, k-nearest neighbors (KNN), support vector machine (SVM), Naive-Bayesian classifier, random forest and neural network. Results show that the researchers are able to obtain an accuracy of 98.67%, 97.78%, 98.67%, 94.22%, and 99.56% for all the five models respectively, having the neural network to be the best performing model.

Development of Machine Learning-based Predictive Models for Air Quality Monitoring and Characterization

In the Philippines' banana export industry, the postharvest classification of Cavendish banana is an important factor which affects one of the country's revenue. The present set up uses a quality inspector and is solely dependent on its visual capability. This may result in an error and inconsistent results grading classification. This study explores the use of image processing with deep learning approach to classify Cavendish banana grade by the basis of its standard requirement. A total of 1116 bananas comprising of 279 images from each category such as Class A big-hand or small-hand, Class B big-hand or small-hand and Cluster class (part of hand) are used to develop algorithm and classification accuracy by using Phyton OpenCV and Tensorflow. The sample images follow four stages: Image Thresholding, Feature Extraction, Classification Prediction, and Testing. An analysis of predicted accuracy in all classes (A and B) are more significant in the finger size value extraction (small or big hand) than in surface defects value extraction. The final classification in all trained, validation, and test data showed above 90% accuracy in all four classes. Hence, the proposed CNN classification in Tensorflow model can be commercially developed as a field-based complete automatic postharvest classification system for grading a Cavendish banana.

Jennifer C. Dela Cruz

Papers

White blood cell classification and counting using convolutional neural network

Identification of diseases in rice plant (oryza sativa) using back propagation Artificial Neural Network

Soil pH and nutrient (Nitrogen, Phosphorus and Potassium) analyzer using colorimetry

Development of Machine Learning-based Predictive Models for Air Quality Monitoring and Characterization

Postharvest Grading Classification of Cavendish Banana Using Deep Learning and Tensorflow