Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm
The project aims to revolutionize disease monitoring through the use of artificial intelligence (AI), advanced sensor technology, and crowd-sourcing, connecting the global agricultural community to support smallholder farmers. Specifically, a Convolutional Neural Network (CNN) was employed in this study. CNN models offer promise in enhancing plant disease phenotyping, where traditional methods rely on visual diagnostics requiring specialized training. Deploying CNNs on mobile devices presents new challenges such as varying lighting conditions and orientations. Therefore, evaluating these models under real-world conditions is crucial for their reliable integration into computer vision tools for plant disease assessment.
Our approach involved training a CNN object detection model to identify foliar disease symptoms in cassava (Manihot esculenta Crantz). Subsequently, we implemented the model in a mobile application and assessed its performance using images and videos captured in an agricultural field in Nigeria, totaling 720 diseased leaf samples. We conducted tests for two severity levels of symptoms—mild and pronounced—within each disease category to evaluate the model’s effectiveness in early symptom detection.
Across both severity levels, we observed a decline in performance metrics, specifically the F-1 score, when analyzing real-world images and video data. Notably, the F-1 score decreased by 32% for pronounced symptoms in real-world images, primarily due to reduced model recall. Our findings underscore the importance of fine-tuning recall metrics to achieve desired performance levels in practical settings if mobile CNN models are to fulfill their potential. Additionally, the varying performance outcomes between image and video inputs highlight critical considerations for designing applications intended for real-world deployment
Cover page
Title page
Approval page
Dedication
Acknoweldgement
Abstract
Chapter one
1.0 introduction
1.1 Background of the study
1.2 Problem statement
1.3 Aim and objective of the study
1.4 Significance of the studyt
1.5 Project organisation
Chapter two
Literature review
2.1 Introduction
2.2 Review of the study
2.3 Overview of cassava
2.4 Review of different types of cassava diseases
Chapter three
3.1 Materials and method
Chapter four
4.1 Result
4.2 Data preprocessing
4.3 Cnn model
Chapter five
5.1 Discussion and conclusion
5.2 Recommendation
5.3 References
The Title Page should be the first section of your project “Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm”, providing essential details like the project title, your name, your supervisor’s name, the institution, and the submission date. After that, the Abstract offers a brief summary of your project, touching on its purpose, methods, results, and conclusions in 150-300 words. The Acknowledgments section is where you can thank those who supported your research, such as your supervisor, peers, or organizations that provided resources.
Next, the Table of Contents organizes the Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm by listing its chapters and sections, along with page numbers for easy reference. The List of Figures and List of Tables help guide readers to specific visual elements like graphs, charts, or tables included in the document. There should also be an Abbreviations and Glossary section to explain any specialized terms or acronyms, making the content clearer to readers unfamiliar with the technical language.
The main body of the Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm should start with the Introduction, which provides background information, outlines the research problem, states your objectives, and gives a brief overview of your research methods. Following that, the Literature Review offers an in-depth look at previous research relevant to your project, identifying gaps your study aims to address. The Methodology section then explains the research design, tools, and data collection methods you used to conduct the project and analyze the data.
In the Results and Discussion section, you present your findings and discuss them in relation to the Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm research questions or objectives, often using tables or charts to help explain the data. The Conclusion summarizes the key results, discusses their implications, and suggests possible directions for future research. You may also include recommendations based on your findings, offering practical advice for improvements or applications. Finally, the Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm project should include a References or Bibliography section to list all the sources you cited, as well as Appendices for any additional material. A Statement of Originality is often included to confirm the authenticity of your work