Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm
Complete Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm Project Materials (Chapters 1 to 5):
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 introduction of Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm should start with the relevant background information of the study, clearly define the specific problem that it addresses, outline the main object, discuss the scope and any limitation that may affect the outcome of your findings
Literature Review of Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm should start with an overview of existing research, theoretical framework and identify any gaps in the existing literature and explain how it will address the gaps
Methodology of Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm should describe the overall design of your project, detail the methods and tools used to collect data explain the techniques used to analyse the collected data and discuss any ethical issues related to your project
Results should include presentation of findings and interpretation of results
The discussion section of Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm should Interpret the implications of your findings, address any limitations of your study and discuss the broader implications of your findings
The conclusion of Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm should include summarize the main results and conclusions of your project, provide recommendations based on your findings and offer any concluding remarks on the project.
References should List all the sources cited in Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm project by following the required citation style (e.g., APA, MLA, Chicago).
The appendices section should Include any additional materials that support your project (Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm) but are too detailed for the main chapters such as raw data, detailed calculations etc.