Ontology Based Expert System for Pests and Disease Management of Cotton Crop in India

2018 ◽  
Vol 10 (2) ◽  
pp. 32-49
Author(s):  
Mahesh D. Titiya ◽  
Vipul A. Shah
Keyword(s):  
Web Services ◽  
Soil Health ◽  
Agricultural Practices ◽  
Weather Data ◽  
Application Development ◽  
Domain Experts ◽  
Related Data ◽  
Main Challenge ◽  
Main Components ◽  
Restful Web Services

In the agricultural domain, the main challenge is to present the new information and research to the farmers so that they can leverage the power of technologies to improve their agricultural practices and thereby the production. Huge amounts of agriculture-related data like weather data, soil health records, disease and pest are collected from different sources like web services, remote satellites, and a network of sensors. The authors' ontology-based agro advisory system will help to bridge the gap between farmers and the agriculture domain experts. It has three main components: Cotton Ontology, Web services and Mobile Application Development. Protégé tool is used to develop ontology. The RESTful web services are programmed in Java using the JAX-RS/Jersey API and Eclipse IDE. RESTful web services are all developed and deployed on a cloud-based application server provided by Heroku. The farmers can access an application by android mobile. The Android user interface is created using Java, Android SDK-v1.4 and Eclipse IDE.

scholarly journals Resource and dependency based test case generation for RESTful Web services

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Man Zhang ◽  
Bogdan Marculescu ◽  
Andrea Arcuri
Keyword(s):  
Web Services ◽  
Domain Knowledge ◽  
Automated System ◽  
Test Case ◽  
Test Cases ◽  
Evolutionary Search ◽  
Sampling Strategies ◽  
Mutation Operators ◽  
On Line ◽  
Restful Web Services

AbstractNowadays, RESTful web services are widely used for building enterprise applications. REST is not a protocol, but rather it defines a set of guidelines on how to design APIs to access and manipulate resources using HTTP over a network. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services, by exploiting domain knowledge on the handling of HTTP resources. The proposed techniques use domain knowledge specific to RESTful web services and a set of effective templates to structure test actions (i.e., ordered sequences of HTTP calls) within an individual in the evolutionary search. The action templates are developed based on the semantics of HTTP methods and are used to manipulate the web services’ resources. In addition, we propose five novel sampling strategies with four sampling methods (i.e., resource-based sampling) for the test cases that can use one or more of these templates. The strategies are further supported with a set of new, specialized mutation operators (i.e., resource-based mutation) in the evolutionary search that take into account the use of these resources in the generated test cases. Moreover, we propose a novel dependency handling to detect possible dependencies among the resources in the tested applications. The resource-based sampling and mutations are then enhanced by exploiting the information of these detected dependencies. To evaluate our approach, we implemented it as an extension to the EvoMaster tool, and conducted an empirical study with two selected baselines on 7 open-source and 12 synthetic RESTful web services. Results show that our novel resource-based approach with dependency handling obtains a significant improvement in performance over the baselines, e.g., up to + 130.7% relative improvement (growing from + 27.9% to + 64.3%) on line coverage.

scholarly journals Application of Nematode Community Analyses-Based Models towards Identifying Sustainable Soil Health Management Outcomes: A Review of the Concepts

Soil Systems ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 32
Author(s):  
Haddish Melakeberhan ◽  
Gregory Bonito ◽  
Alexandra N. Kravchenko
Keyword(s):  
Nutrient Cycling ◽  
Health Management ◽  
Management Strategies ◽  
Soil Health ◽  
Agricultural Practices ◽  
Nematode Community ◽  
Parasitic Nematodes ◽  
Worst Case ◽  
Pests And Diseases ◽  
Water Holding

Soil health connotes the balance of biological, physicochemical, nutritional, structural, and water-holding components necessary to sustain plant productivity. Despite a substantial knowledge base, achieving sustainable soil health remains a goal because it is difficult to simultaneously: (i) improve soil structure, physicochemistry, water-holding capacity, and nutrient cycling; (ii) suppress pests and diseases while increasing beneficial organisms; and (iii) improve biological functioning leading to improved biomass/crop yield. The objectives of this review are (a) to identify agricultural practices (APs) driving soil health degradations and barriers to developing sustainable soil health, and (b) to describe how the nematode community analyses-based soil food web (SFW) and fertilizer use efficiency (FUE) data visualization models can be used towards developing sustainable soil health. The SFW model considers changes in beneficial nematode population dynamics relative to food and reproduction (enrichment index, EI; y-axis) and resistance to disturbance (structure index, SI; x-axis) in order to identify best-to-worst case scenarios for nutrient cycling and agroecosystem suitability of AP-driven outcomes. The FUE model visualizes associations between beneficial and plant-parasitic nematodes (x-axis) and ecosystem services (e.g., yield or nutrients, y-axis). The x-y relationship identifies best-to-worst case scenarios of the outcomes for sustainability. Both models can serve as platforms towards developing integrated and sustainable soil health management strategies on a location-specific or a one-size-fits-all basis. Future improvements for increased implementation of these models are discussed.

scholarly journals A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2514
Author(s):  
Tharindu Kaluarachchi ◽  
Andrew Reis ◽  
Suranga Nanayakkara
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Review Paper ◽  
Learning Systems ◽  
Learning Approaches ◽  
Application Development ◽  
Research Gaps ◽  
Domain Experts ◽  
Working Definition ◽  
Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

scholarly journals Dewpoint Temperature Prediction Using Artificial Neural Networks

2008 ◽  
Vol 47 (6) ◽  
pp. 1757-1769 ◽  
Author(s):  
D. B. Shank ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Heat Waves ◽  
Weather Data ◽  
Temperature Prediction ◽  
Related Data ◽  
Dewpoint Temperature ◽  
Artificial Neural ◽  
Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

Impact of Agricultural Practices on Soil Health

2020 ◽  
pp. 89-98
Author(s):  
Elizabeth Temitope Alori ◽  
Aruna Olasekan Adekiya ◽  
Kehinde Abodunde Adegbite
Keyword(s):  
Soil Health ◽  
Agricultural Practices

scholarly journals Implementation of Smart Mirror using Raspberry PI

2021 ◽  
pp. 234-238
Author(s):  
Aditya Tepalwar ◽  
Asha Sherikar ◽  
Prajyot Mane ◽  
Vishal Fulpagare
Keyword(s):  
Web Services ◽  
Information Services ◽  
Mirror System ◽  
Weather Data ◽  
Raspberry Pi ◽  
The Internet ◽  
Computer Assisted ◽  
User Data ◽  
Information And Communication ◽  
Smart Appliance

Smart appliance design that includes multimedia intelligence to deliver comfortable, convenient, and secure personal services in the home is becoming increasingly crucial in the age of information and communication technology. This research looks at the design and execution of a novel interactive multimedia mirror system called as "smart mirror." The glass that will be used is the foundation of the design of a smart mirror. Two-way glass is suggested because it allows the visuals on the display to be seen more clearly. Our way of life has evolved to the point where making the best use of one's time is critical. Based on user surveys and prototype implementation, we propose the development of an innovative appliance that incorporates interactive information services delivered via a user interface on the surface of a mirror. Our work is based on the assumption that we all check ourselves in the mirror before leaving the house, so why shouldn't the mirror be intelligent? Smart Mirrors will eventually replace regular mirrors, providing users with both mirror and computer-assisted information services as technology improves. Because of the Raspberry Pi microcontroller cards aboard, the devices can connect to the internet, download data from the internet, and show that data on the mirror. Weather data, time and location data, current event data, and user data gathered from web services using a Raspberry Pi 3 microcontroller card are all included in the designed intelligent mirror system. The mirror will light up when the user steps in front of it. When thinking about this project, phrases like Smart Mirror, Interactive services, Raspberry Pi , and Web services come to mind.

scholarly journals Climate Resilient Sustainable Agriculture for Restoring the Soil Health and Increasing Rice Productivity as Adaptation Strategy to Climate Change in Indonesia

2021 ◽  
Vol 748 (1) ◽  
pp. 012039
Author(s):  
Tualar Simarmata ◽  
M Khais Proyoga ◽  
Diyan Herdiyantoro ◽  
Mieke R Setiawati ◽  
Kustiwa Adinata ◽  
...  
Keyword(s):  
Climate Change ◽  
Sustainable Agriculture ◽  
Smallholder Farmers ◽  
Soil Health ◽  
Agricultural Practices ◽  
Adaptation Strategy ◽  
Water Efficiency ◽  
Rice Productivity ◽  
Rice Intensification ◽  
Significant Yield

Abstract Climate change (CC) is real and threatens the livelihood of most smallholder farmers who reside along the coastal area. The CC causes the rise of temperature (0.2-0.3°C/decade) and sea level (SRL = 5 mm/year), drought and floods to occur more frequently, the change of rainfall intensity and pattern and shifting of planting season and leads to the decreasing of crop yield or yield loss. Most of the paddy soil has been exhausted and degraded. About 50% of the rice field along the coastline is effected by high salinity and causes significant yield losses. The research was aimed to summarize the results of the system of organic based aerobic rice intensification (known as IPATBO) and of two climate filed school (CFS) in Cinganjeng and Rawapu that situated along the coastline of Pangandaran and Cilacap. Both IPATBO and CFS have adopted the strategy of climate-resilient sustainable agriculture (CRSA) for restoring the soil health and increasing rice productivity, and as well as to empower the farmer community. The implementation of IPATBO (2010-2020) in the different areas has increased the soil health, fertilizers, and water efficiency (reduce inorganic by 25-50%, and water by 30-40%) and increased rice productivity by at least 25-50%. Both CFS in Ciganjeng and Rawaapu were able to improve soil fertility, increase rice productivity, and farmer capacity. This result concludes the agro-ecological based CRSA and CFS can be adopted for the increasing the resilient of agricultural practices and farmers in adapting to climate change

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