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Impact of Weather on Crops in Few Northern Parts of Bangladesh: Hci and Machine Learning Based Approach

Md. Toukir Ahmed*
Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

Md. Niaz Imtiaz
Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

Nurun Sakiba Mitu
Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

Abstract/ Executive Summary:

As Bangladesh is an agricultural country, the economy as well as food security of this country mostly depends on production level of different crops over the year. Therefore, there exists immense pressure on exaggerated crop production due to fast growth of population. But, average production level is being hampered by bad nature of weather. We have conducted a survey on near about 100 farmers of two northern districts of Bangladesh: Pabna and Rajshahi and assessed impact of rough nature on production. According to farmers and agriculturalists, it is noticed that rough weather causes about 30% to 70% production shortage than expectation with all other factors remaining constant. In this study, we have adopted Human computer interaction (HCI) based approach (Soft System Methodology) to this aspect for efficacious collaboration with root level farmers and agricultural trainers providing ease for understanding weather related issues on production of crops. Finally, some machine learning algorithms were also implemented on the obtained dataset to accurately classify the range of production level of rice and a comparison is made among the algorithms based on performance metrics. Moreover, an android based application is created to depict the summary of the study.

Keywords: HCI, Machine learning, Performance metrics, SSM, Weather, Impact, Northern parts.

Location:

Start Date:

End Date:

Program Area: Knowledge Management

Non-commodity: Weather/Climate

Objectives:

We have made a mobile app using Android application which shows the data and suggestion as table or graphically or as text. Our research paper extends the work of HCI to the Machine Learning. We used Weka and classified various model which shows different accuracy (Islam et al., 2020). And finally, we compared different algorithms to find out the best performing algorithm.

Methodology:

Human-computer interaction (HCI) researches are concerned with design and usage of computer technology, focused largely on the interfaces between people (users) and computers. Researchers in the field of HCI both peruse the different styles in which humans interact with computers and design technologies that let humans collaborate with computers in unique ways (Dix, 2003). HCI puts more weight on users working specifically with computers, compare to other kinds of machines or designed artifacts (Dix, 2003).There is also a focus in HCI on how to implement the computer software and hardware mechanisms to support human-computer interaction (Dix, 2003). A. CUSTOM methodology A socio-technical methodology designed to be practical to use in small organizations, is known is a CUSTOM methodology (Andrew Faulring, 2012). It is supported the User Skills and Task Match (USTM) approach, developed to allow design teams to understand and fully document user requirements. CUSTOM focuses mainly on establishing stakeholder requirements: all stakeholders are considered, not just the end-users. It is applied at the initial stage of design when a product chance has been identified, so the emphasis is on capturing requirements. B. Soft Systems Methodology (SSM) The socio-technical models we have looked at focus on identifying requirements from both human and technical directions, but they assume to be proposed a technological solution (Anita Mirjamdotter, 2009). Soft systems methodology (SSM) comes from the same identical tradition but takes a view of the organization as a system of which technology and people are the key components. There is no presumption of a specific solution: the emphasis is rather on understanding the situation fully. SSM was developed by Check-land. The first stage of SSM is the recognition of the problem and initiation of analysis. This is followed by a detailed description of the problem situation: developing a rich picture. This will comprise with all the stakeholders, the day to day tasks they carry out and the groups they work in, the organizational structure and its processes and the issues constructed by each stakeholder. Sequential Minimal Optimization Sequential Minimal Optimization (SMO) is a SVM (support vector machine) driven algorithm. Perception learning algorithm is essentially doing same thing find a linear separator by adjusting weights on misclassified examples. Unlike perception, SMO has to maintain sum over examples of example weight Ames example label (Russell, 2002). J48 It is a machine-learning based predictive model which decides the target value of a new sample based on various attribute values of the available data is J48 decision tree. The different attributes denoted by the internal nodes of a decision tree, the branches between the nodes tell us the possible values that these attributes can have in the experimental samples, while the terminal nodes tell us the final value of the dependent variable (Russell, 2002). Multiclass classifier In multiclass classification, each record belongs to more than two classes, and the algorithm’s goal is to design a function which, given a new data point, will correctly classify data. Multiclass classification incorporates the number of pragmatic business problems; researchers can gain insight into with machine learning. This is just one example of a business use case for multiclass (Russell, 2002).

Source of Information: Australian Journal of Engineering and Innovative Technology, 2(1), 7-15, 2020

Article Link (Online Journal): https://doi.org/10.34104/ajeit.020.07015

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Achievement/Findings:

This paper contributes to HCI by expanding the impact of weather on crops in case of Bangladesh (Rajshahi and Pabna). We have explained the problems and experience of farmers. We have also pointed out suggestions of officers of different sectors. Though, the implemented model will be helpful to calculate the accurate percentage of production of various crops with the change of weather, we needed large number of data for implementing the model. But, as most of the farmers are not literate enough, they could not reply properly to our questionnaire. So, we had to discard some of the data as they were not adequate. This was the main limitation of our work. In future we want to build an artificial intelligence based interactive application collecting more data, that will be beneficial to the agricultural sector of those region and would be handful for the farmers for good growth of appropriate crops based on weather.

Knowledge Management: Journal