Design and implementation for the prediction of agricultural yield

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Price: 3000 Naira (BSC, MSC)

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study
According to Rafea (2016), expert systems were identified eight years ago, by the Egyptian Ministry for Agriculture (MOA), as an appropriate technology for transferring knowledge and expertise predictions of agricultural specialists to extension service. In 1989, the Expert Systems for Improved Crop Management (ESICM) project was initiated with two main objectives: building an expert system laboratory within MOA that has the capacity to identify, develop, and maintain expert systems, and second developing two expert systems for the production management of cucumber under plastic tunnels, and citrus in the open field.
According to Jones (2016), agricultural systems as we know it today has evolved over the last 50 or more years with contributions from a wide range of disciplines. Generally during this same time period, appreciation for and acceptance of agricultural systems science has increased as more scientists, engineers, and economists graduate from universities with training in systems modeling, analytical approaches, and information technology (IT) tools. Over this time period, there has also been a corresponding increase in demands for agricultural systems science to address questions faced by society that transcend agriculture. Relevant questions range from how to better manage systems for higher and more efficient production, what changes are needed in a farming system for higher profitability without harming the environment, how can agricultural yield be predicted by systems, what policies are needed to help farming systems evolve to meet broader societal goals, and what systems are needed to adapt to the continual changes that agriculture faces, including climate change, changes in demand for agricultural products, volatile energy prices, and limitations of land, water, and other natural resources.
Agricultural systems models are being challenged to move beyond just including economic and sustainability issues. There is a strong agenda of new Sustainable Development Goals, which will require models of nutritional quality of food beyond bulk yields and multifunctional landscape models for policy analyses. According to Scott (2015) sustainable solutions that address multiple goals will likely benefit from a convergence of science and technologies that make use of information and cognitive sciences. In order to analyze these different dimensions of agriculture and food systems, ideally, we would have a virtual laboratory containing models, data, analytical tools and IT tools to conduct studies that evaluate outcomes and tradeoffs among alternative technologies, policies, or scenarios. The virtual laboratory would allow users to define scenarios, specify analyses covering different social, political, and resource situations and different spatial and temporal scales, and produce outputs suitable for interpretation and use by decision makers. Clearly, that virtual laboratory does not exist. But where are we currently relative to this ideal situation? The purpose of this research work is to develop an expert system to predict agricultural yield
Problem Statement
According to Turban (2017), an Expert System (ES) is a computer-based information system that use expert knowledge to attain high level decision performance in a narrowly defined problem domain. Expert systems can be used as a solution to overcome the scarcity of experts, particularly in the prediction of agricultural yield as most agricultural experts face the problem of making a precise prediction of their agricultural yield which in turn leads to all sorts of stress and problems for them. Today, the problem facing most entrepreneurs and other people in sectors interested in agricultural produce is the fact that there are limited personnel that have the adequate knowledge needed to make a precise prediction of an agricultural yield, most of them have little or no knowledge about the way, process and manner in which agricultural produce yield, which makes it difficult for them to go deep into the agricultural business sectors as it tends to be difficult for them to be fully invested in what they have little or no knowledge about the yield. The expert system for the prediction of agricultural yield that have been developed in the past have only been able to focus on just the time frame it takes for an agricultural produce to yield, but they haven’t gone in-depth on focusing on the sort of climate it takes for it to yield at that point in time, under what atmospheric condition, how it can be cultivated and produced, although, there have been studies that have been focused on developing expert systems in different departments but none has ever been focused on developing an expert system for the prediction of agricultural yield in all spheres stated above, it is against this backdrop that this project work intends to develop an expert system for the prediction of agricultural yield.
1.2 Aim and Objectives of the Study
Aim
The aim of the study is to develop an expert system for the prediction of agricultural yield
Objectives of the Study
the following are the specific objectives:
i. To design a system that will aid the prediction of agricultural yield.
ii. To develop a system that can serve as a medium for predicting agricultural yield with a particular focus on Yam, Cassava and Maize.
iii. To implement a system that will provide agricultural knowledge of different crops.
iv. To test and evaluate the designed system in ensuring that the database that contains information with respect to prediction of agricultural yields is accurate, secured and up to date at all times.
1.3 Proposed Methodology
• The expert system for the prediction of the agricultural yield will be a web based designed application.
• This website will be designed as an interactive software tool for predicting the influence of climatic parameters on the crop yields.
• ID3 and C 4.5 algorithms introduced by Quinlan for inducing classification models, also called decision trees from data if we are given a set of records. Each record has the same structure, consisting of a number of attributes, value pairs. One of these attributes represents the category of the record which will be used to find out the most influencing climatic parameter on the crop yields of selected crops in selected districts of the researcher’s location. The users however, can change the data sets (input parameters) suiting to their study area. For using this tool, the users will be able to register their details on the website, with a confirmation from the administrator; users will be free to use the tool.
• The software will provide an indication of relative influence of different climate parameters on the crop yield, other factors responsible for crop yield are not considered in this tool.
• The development tools used in the implementation include:
i. Xampp
ii. Html, CSS, Php
iii. JavaScript
iv. MySQL for database

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