CHAPTER ONE

INTRODUCTION

1. 1. Background of the Study

Yam (Manihotesculentumcrantz), is the third largest source of carbohydrates for human consumption worldwide, providing more food calories per cultivated acre than any other staple crop. It is an extremely robust plant which tolerates drought and low-quality soil. The foremost cause of yield loss for this crop is viral disease.  The plant grows in a bushy form, up to 2.4 meters high, with greenish-yellow flowers. The roots are up to 8 centimeters thick and 91 centimeters long. Two varieties of the yam are of economic value: the bitter, or poisonous; and the sweet, or non-poisonous. Both varieties yield a wholesome food because the volatile poison can be destroyed by heat in the process of preparation (Sabine et al. 2017).

Yam is the chief source of tapioca, and in South America a sauce and an intoxicating beverage are prepared from the juice. The root in powder form is used to prepare farinha, a meal used to make thin cakes sometimes called yam bread. The starch of yam yields a product called Brazilian arrowroot. In Florida, where sweet yam is grown, the roots are eaten as food, fed to stock, or used in the manufacture of starch and glucose. The economies of many developing countries are dominated by an agricultural sector in which small-scale and subsistence farmers are responsible for most production, utilizing relatively low levels of agricultural technology. As a result, disease among staple crops presents a serious risk, with the potential for devastating consequences. It is therefore critical to monitor the spread of crop disease, allowing targeted interventions and foreknowledge of famine risk (Al-Hiary et al., 2015).

There are many yam farmers across Nigeria. Unfortunately, the existing (Agric officers) are not sufficient to provide needed services in yam leaf disease detection. It is imperative to integrate their services in the effort of farmers to enhance yam production and yam diseases detection in leaves. Hence, the need for a mobile based computing program that is crucial in the classification of yam diseases via leaf. This forms the basis for initiating an alternative strategy by developing a soft computing system for the early detection of yam diseases in yam leaves. The development in information technology over the past few decades are tremendous and offer great potential in improving agricultural products through various measures (Bashir et al., 2017).

1.2     Statement of the Problem

Leaf diseases are economically critical as they can be a matter of a loss of yield. Initial and trustworthy detection of leaf diseases has an important practical application, especially in the background of precision farming for confined treatment with fungicides. Amid the last few years, image categorization has proved increasingly effective in agriculture and biology, as numerous tasks have been simplified with the Support of automated snapshot classification. Conventional master frameworks particularly those utilized as a part of diagnosing maladies in agricultural domain depend only on textual information. Generally, abnormalities for a given crop are manifested as symptoms on various parts of the plant such as the leaves. To implement a specialist system to produce right results, end clients must be capable of mapping what they see in a form of unusual manifestations to answers to questions asked by that master framework. This mapping may be inconsistent if a full Knowledge of the anomalies on any plant. Contingent upon the client’s level of comprehension of the unusual Perceptions, the professional system can reach the correct diagnosis. The unusual scrutinization in a incorrect way and selects a wrong textual answer to a given question, and then the expert framework will achieve a wrong reply. a propose technique where irregularities are mechanically perceived, would diminish the threat of human blunder. Convolutional Neural Networks (CNN), a class of deep learning (DL) model with end-to-end feature extraction and classification promises highly scalable and superior results on yam leaf disease classification.

1.3     Aim and Objectives of the Study

The aim of this study is to develop a yam leaf disease classification system using Deep Learning. The objectives of the study are as follows;

1. To Design a convolution neural network (CNN) model for yam leaf disease detection.
2. To Implement the system in (i) using java programming.
3. Deploy the system in (ii) on an android mobile device.
4. To perform functional test on the system deployed in (iii).

1.4     Significance of the Study

This study will serve as an avenue that yam farmers can use to carry out yam leaf disease classification using their mobile device.

The software is also relevant in the following ways:

1. Enable easy classification of yam leaf disease.
2. Reduce the cost of hiring an expert to do the job of detecting yam leaf diseases.
3. Reduce the time of carrying out a yam leaf diseases classification.

1.5     Scope of the Study

This research is limited to the classification of yam disease on its leafs by using computer vision and convolutional Neural Network on a mobile device.

1.6   Limitation of the Study

The researcher faced some limitations during the course of this research. Some of this limitations/constraint includes;

i.        Time constraint: Time factor was the major factors as the timeframe to submit the project was too short for a rigorous research.

ii.       Financial constraint:  Lack of adequate finance to fund the researcher as to the purchase of data to review related works online was another major setback.

1.7     Definition of Terms

1. Artificial Intelligence: It is an intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals.
2. Artificial Neural Networks:    These are computing systems that are inspired by, but not necessarily identical to biological neural networks, that constitute animal brain. Such systems learn to perform task by considering examples, generally with being programmed with any task or specific rules.
3. Convolutional Neural Network (CNN):   Is a class of Deep Neural Networks, most commonly applied to analyzing visual imagery.
4. Data Argumentation:   Is use in deep learning to regularize, normalize the information on data to be trained.
5. Data Notation:    This is the process of labeling data to make it usable for machine learning.
6. Dataset:      A collection of related sets of information that is composed of separate elements but can be manipulated as a unit by a computer.
7. Deep Learning: It is a part of broader family of machine learning model base on artificial neural networks.
8. Disease Detection:    It is a process of determining which disease or condition explains a tree/persons symptoms and sign. It is most often referred to as diagnosis with the medical context.
9. Disease Diagnosis:   It is process of determining which disease or condition explains a tree/persons symptom and signs.
10. Image:     This refers to the optical counterpart of an object produced by an optical device (such as a lens or mirror) or an electronic device.
11. Machine Learning: It is a scientific study of algorithms and statistical model that computer system uses in other to perform a specific task effectively, without using an explicit instruction relying on patterns and inference instead. It is seen as subset of artificial intelligence.
12. Yam: An edible, starchy, tuberous root of a plant usually cultivated.
13. Crop: A plant grown to be harvested as food, livestock fodder, or fuel for any other economic purpose.
14. Pest: A plague, pestilence, epidemic, harmful destructive creature.
15. Mobile App:  A mobile app or mobile application is a computer program or software application designed to run on a mobile device such as a phone-tablet or watch.
16. Model: Is a description or analogy used to help visualize something that cannot be directly observed.
17. Multilayer Perceptron: This refers to fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer.
18. Pathogen:   It is a bacterium, virus, or other microorganism that can cause disease.
19. Perceptron: This refers to a linear classifier (binary) use in supervised learning. It helps to classify the given input data.
20. Software:   It is a program and other operating information use by a computer system.
21. Tensor-Flow: is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.