12.1 Introduction

Mobile cloud computing is the emerging technology that has recently been used in many sectors, such as transportation, education to enhance their growth and productivity. With the advent smartphones, this technology can be extended to a great extend so that its advantage could be spread to the majority of the peoples. It is well known that a majority of the people in India depend upon agriculture, and uncertainty in weather conditions affects crops’ productivity to a great extent because the farmers are mainly not aware of this condition and due to lack of information. They are not able to tackle the present situation. With the advent of ICT (Information and Communication Technology) and cloud computing, it can be used to provide all the valuable information directly to the farmers and guidance from top agricultural institutes from which farmers could interact and solve their problems in real-time.

Cloud computing will enable to host of the data related to agriculture, which is processed by the app engine, which in turn will be provided in the mobile application. The execution of data in the cloud employing computation offloading and App engine presence in the cloud will process all the relevant information from different websites related to the agricultural sector. Any pertinent information will be provided to the user associated with the agricultural sector, and this will help the user save his time and utilize that time in some productive work. He has not to search for the information from a different website. The other means of the communication system will enable the user to save internet data and computational resources, which in turn reduces the power consumption of the application. It is installed on the user’s smartphone. The user interface is also interactive and easy to understand. It does not require enough technical knowledge. The user has to know basic things like how to access the data and derive useful results.

This work aims to use cloud computing as a tool for ICT and provide essential information related to Agriculture in real-time to farmers and saving Energy by offloading computation through MCC (Mobile cloud computing) in the form of cloudlets from Mobile handset to the cloud server

• To provide information on the availability of new tools and technologies related to the agricultural sector which will help in assisting the farmers for better cultivating methodology and also offer training material of new tools online without wasting their time in giving to a different place for consulting the and learning how to use that specific tool effi-ciently in agriculture and growth of crops.
• It is providing Introduction to new farming practices and its advantage over the traditional method.
• To provide real-time information related to climate, diseases and pests, harvesting mechanisms, post-harvest strategies, and finally, proper marketing.
• Expert advice based on the uploaded image of the crops.
• Cost reduction mechanism by providing information on new government schemes.

The rest of the paper is organized as follows: Motivation is introduced in Section 12.2. Related work is present in Section 12.3. Proposed methodology and experimental results discussion in detail is discussed in Section 12.4. In the end, conclusion and future work are devoted to Section 12.5.

12.2 Motivation

The motivation for this work is noted below.

• There is no reliable information system existing that could be used by both farmers and researchers in the agricultural sector to get real-time updates and information to enhance productivity and knowledge in agriculture.
• Accessing websites on the internet is too time and energy- consuming to get different information. A person has to access a separate website that is time-consuming, and sometimes the relevant information is not present on a particular site, so the app-based information system application will fetch most of the informative website and group them in order time in which they are updated. So that the user accesses all the relevant information. It shows the tag or the heading of the information, which contains the link from which the user can read the complete information.
• This application does the computation in the cloud, so it is platform-independent and does not consume much of the Smartphone’s energy and resources.
• This application can access much information from the internet, which can help in enhancing productivity. It will help the farmers to interact with experts from the agricultural sector.

12.3 Related Work

Rose et al. [1] have used a decision support tool, which is software for evidence-based decision making in agriculture to improve productivity and environmental outputs. By combining qualitative interviews and quantitative surveys, researchers have found that 15 factors are influential in convincing farmers and suggest using decision support tools. In the UK, this study finds a plethora of agriculture decision support tools in operation.

Lee et al. [2] gives an overview of four national forest fire management information system for Canada. The spatial Fire management system (sFMS) is used to implement Canada’s national forest fire management information systems, the Canadian wild land fire information systems, fire monitoring, mapping, and modeling. It presents daily information on fire weather, fire behavior potential, and selected upper atmospheric conditions.

Zhang et al. [3] have reviewed and identified the ICT based information in china. This study analyses the development stages of china’s agriculture information dissemination systems. The seven ICT-based information dissemination models are identified and discussed. It provides directions for researchers in developing futures ICT-based information dissemination systems. This research article will help other developing countries to apply emerging ICTs in agriculture information dissemination and knowledge transfer.

Ziogas [4] have developed a Farm Management Information System (FMISs) which utilizes new technologies. The developed application is focused upon the individual farmers or farmer cooperatives. The objective of this study is to perform farm financial analysis based on all farm transactions. This application was successfully tested on a winter wheat crop for one season, where all related costs were recorded.

Shahzadi et al. [5] have proposed an expert system based on the Internet of Things (IoT), which will use the input data collected in real-time. It will help obtain proactive and preventive actions to minimize the losses due to diseases and insects/pests.

Prasad et al. [6] have proposed various ways in which farmers can use Mobile Cloud Computing (MCL) on their handsets using an application called Agro Mobile, which helps them for relatively better cultivation and marketing. The main focus is on crop image analysis. Here, the framework uses MCC, by which authors believe that the cloud will be into a farmer’s pocket. The framework has been tested on Android-based mobile devices.

Dahikar & Rode [7] have used Artificial Neural Network (ANN) for modeling and prediction of the crop. It is used to predict a suitable crop by sensing various soil parameters and related to the atmosphere.

Karetsos et al. [8] have reviewed the use of smartphones and capabilities in agriculture. They have proposed a transactional m-government app for agriculture as an add-on to an existing government portal. The app is easy to access and promising solutions for farmers.

Pal et al. [9] have proposed a voice-based mobile application for agricultural commodity price dissemination in the Bengali language. The automatic speech recognition incorporated app provides an excellent value- addition to the existing websites of the agriculture marketing department of the West Bengal government and Indian government.

Pongnumkul et al. [10] have reviewed many articles applications that use built-in Smartphone sensors to provide solutions to agriculture. They have focused on how smartphone sensors have been used in agriculture, without the need for external sensors.

Agrawal & Sattiraju [11] have designed two smartphone applications for farmers of Indian agriculture.

Zen et al. [12] have used geographical Information System (GIS) techniques to analyze various types of geospatial data and deal with complex situations.

Roy et al. [13] have used a Cordova framework based geo package mobile/application to support field applications in agriculture. After the implementation of geo package SDK on a mobile application, users can easily access, manage, and visualize.

Roy [13] have presented an innovative hybrid Agro Tick system for smart agriculture. Agro Tick is an IoT based system designed to improve the efficiency of agriculture.

Nugroho et al. [14] have used web-based monitor systems to monitor the oil palm plantation developed using the Progressive Web App (PWA) approaches. The PWA approach provides easy access for both field employees and plantation supervisors.

Marimuthu et al. [15] have developed the persuasive Technology Method (PTM) to change the farmers’ mindset towards technology supported farming. The PTM with ICT models assessing their success with the farmers.

Rajeshwari et al. [16] have used IoT device to sense the agricultural data and is stored into the cloud database. Here, the data mining techniques have been used for the prediction by information reaches the farmer via mobile application. The objective of this study is to increase crop production and control of agricultural cost.

Kuang et al. [17] have presented a cloud platform for farmland environment monitoring systems by which remote control and real-time alarming is realized through the mobile terminal. The cloud platform has provided strong technical support for the realization of smart agriculture.

Vijay et al. [18] have proposed an aggro-app based improved monitoring system for better production of crops. The objective of this study is to enhance the productivity of crop production. Choubey et al. [19] have used the cloud for image processing.

Rabello et al. [20] have developed a mobile application to optimize the drone flight in a precision agriculture scenario.

Rupanagudi et al. [21] have discussed a novel approach, notably video processing, cloud computing, and robotics, to solve the problem by continually monitoring crops. It has implemented to detect pests in one of the most popular fruits in the world-the tomato.

Rachana & Guruprasad [22] have reviewed the emerging challenges, threats, and concerns in cloud computing security. The study of this article is focused on cloud computing security framework, problems, possible strategies, and technical support.

Patel & Patel [23] have demonstrated how android apps of agricultural services have impacted the farmers in their farming activities.

12.4 Proposed Methodology and Experimental Results Discussion

The methodology of computation of flooding and information sharing are noted in steps which are as follows:

1. The Google cloud platform is used as Platform as a Service for proving resources for computation and data migration in the form of cloudlets from mobile app to gcloud servers with the help of the project creation tool. It provides project id on the cloud platform.
2. All the websites have the Html and XML data contents in which information is stored. The app accesses these websites and does computation offloading in which the computation is sent in the form of cloudlets to the cloud server from the smartphone. The data sent is received by the app engine, which does XML parsing and extracts useful data from the websites which have the latest information in the field of agriculture.
3. RI (Recent Information) algorithm is used to extract the latest and relevant data by allocating priority to each data and making a stack. It contains essential data at the top, which has the highest priority and groups according to the data’s preference. The information this data is then stored in the virtual repository created in the Google cloud server and extraction is based on the latest available information.
4. The data is saved in the form of a JSON parse tree with the main node containing the main data and its sub-nodes containing subsequent data. The RI algorithm collects the latest and essential data and feeds it into the app engine. The app engine does Xml parsing and fetches the data stored in the virtual database (Figure 12.1).

   

5. The important information extracted by the app engine has fended to the mobile app.
6. This app also contains a sub module which allows users to click the picture of the crop’s soil and send it to the expectations for review to check whether the crop has any diseases or to find which fertilizer to add so that to improve its growth and quality ad also provide a chat module in which the farmers could directly chat with the experts to resolve their issues and improve productivity and growth of their crops and improve the quality of agriculture.
7. This app runs in the cloud helps in saving battery and data of the users, and the users do not need to upgrade the version as it is done directly in the cloud by the person who is managing this mobile app.

The application workings are summarized below:

• When the users start the mobile app, it comes online, gets connected to the internet, and searches for the information from various websites
• The application starts loading the information from the website, and the computation offloading takes place and the app engine in the cloud process and finds important information from the websites
• As there are a large number of information from each website RI algorithm starts
• RI fetches all the data from the site. It compares the time in which it was uploaded along with the priority. It sends that data in the form of an information line to the mobile app, and the user gets the information in the form of notification.

The summa public class feedcompaer extends RecyclerView.ViewHolder {

}rized coding for the above statements is mentioned below:

After the data is processed, it is saved in the real-time database until the user remains online in the app. This way, all the relevant and important information is sent to the user without using much of the user’s Smartphone resources. This consumer’s fewer data and power, so it is always advantageous to use mobile cloud computing for cloud computing and computation is offloading. The computation takes place in the cloud server in real-time, and the user gets the data as soon as he comes online in the application. He has installed on his phone to get information related to agriculture for using it (Figure 12.2).

12.4.3 Energy Analysis for Computation Offloading

The energy analysis has been summarized in Table 12.1 when the application used in a Smartphone.

The following used notations indicate:

• M: Speed of instruction per second of the mobile phone
• C: Speed in instruction per second of the cloud server
• D: Bytes exchanged between the mobile and the cloud
• N: Number of resources for computation
• G: Resources involved in offloading process
• p_tr: Power consumed for sending and receiving data
• α: Constant energy consumption when the idle state
• E: Total energy consumed by the process or consumed when the smartphone is idle+ energy consumed when the process is executing

No of websites accessed	CPU used in percentage	Battery consumption 2400 mAh in hours	Data used in MB
2	42	8.4	0.8
4	48	8.2	1
6	54	7.8	1.6
8	58	7.6	2

Energy is saved when the value of C*M is large, and when the process is offloaded to the app engine in a cloud, the processing capacity increased. So the value of C*M is always large; hence the total energy consumption will be low as compared to the normal execution of the application in the smartphone. During normal execution, the value of N will be large and it will increase as the resources are required during the application execution in a smartphone. It will also increase so this will help in conserving and making the process fast and efficient (Figure 12.3).

• Energy consumption through app engine
• Energy consumption through mobile computation

The battery power consumed is 1500 mah in 8 hours for running the same application, whereas in standard mobile computing, the battery drained is 4000 mah in 8 hours (Figure 12.4).

12.4.4 Virtual Database

A virtual database is a cloud-based database that is hosted in the cloud server. The virtual database is capable of storing a large amount of data. It is stored in JSON format and synchronized in real-time in which the user can read, write, and update the data. It is accessible from any platform, whether it is an android or iOS operation system. The multiple users can access the same database and read, but the administrator only provides the update. The virtual database allows secure access to the data so that only the registered uses can access the database. The virtual database provides advantages over the local database as it can be accessed from any machine from anywhere. The user does not have to maintain it. The administrator of the database can maintain it. The data is stored in several servers, so accessing the data according to requirements can also be done in less time.

Real-time database updates itself whenever any update is done locally. If the update is made when the database is offline, it synchronizes with the virtual databases when it gets online. The database provides flexible rules for updating, maintaining, and accessing the database to the user. The real-time database provides several authentication mechanisms for accessing the database to the user. It is mainly a NoSQL type of database and has different optimization and function compared to the relational database. It contains API, which provides quick access to the database according to user requirements related to the information stored in the database (Figure 12.5).

12.4.5 App Engine

The app engine is the platform as a service module cloud computing platform for hosting and developing. The Google managed application which can run on the cloud platform. The application runs on multiple servers and is capable of doing large computation as the scale of the resources with the existing demand for medium files. It is free but for large computation. Google charges some amount for proving resources for execution in the app engine. The App engine could be used in many languages, such as java, python, PHP. It supports many languages that are required to run several virtual machines. It is also capable of running a web app. It is used as a platform as a service. Its usability is extended to many applications (Figures 12.6, 12.7 and 12.8).

12.4.6 User Interface

The user interface uses a tabbed layout with a scrolling view. The information is presented in the mobile APP, it could be scrolled, viewed, and a piece of particular information could be selected to view it in more detail. The link embedded in the information is processed by the app engine to redirect it to the particular website and open in a format that could be easily read. The important information ends in the form of notification using a firebase notification service. The import information could reach the concerned information as soon as possible by which the user can make use of the information and take advantage from it (Figure 12.9).

12.4.7 Securing Data

The security feature of the Google cloud is useful. It provides an SHA-1 key while creating the project. The project could be linked with the app in the cloud, and no modification can be done from others who are not the authorized person to make changes in the application. It also provides several login methods to the user by using their email address or logging in through social networking websites.

Securing user access is done by allowing only registers users to have access to this application. The security is implemented by allowing a user to use either email password or social sites account to login and use the application to get all the important information (Figure 12.10).

12.5 Conclusion and Future Work

Cloud computing has always been a field of research, and its application is extending in every field. It provides software in-demand platform as a service infrastructure, as a service virtual datable, virtual processing capacity. It enables users to migrate to cloud computation for managing and processing their data. It has also emerged in the field of agriculture. It serves the purpose of information and communication technology along with managing the agriculture data in the cloud and proving them to the users. It has increased in improving and extending the processing capability of the smartphone by extending its processing capacity and saving power consumption. It also assists farmers in enhancing productivity and hence contributing to the development of the country.

MCC is composed of mobile computation and cloud computing in the presence of a computer network as processing power. The Smartphones range from medium to high processing speed in Gigahertz. The processing of large applications could be a challenge, so to take this challenge; Mobile cloud computation comes into play. It extends the processing capacity of smartphones compared to computers. The architecture of MCC enables the users to use large applications by offloading the computation to the cloud servers. It processes the user data by resources in the cloud. It is used in the agricultural sector and will be useful in increasing productivity and helping farmers to expect better growth and profit.

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