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
1.0 INTRODUCTION:
This chapter introduces the Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm and its relevance, states the research problems, research questions, and objectives, provides a background of the study, and should also include the research hypothesis [INTRO4800]…
2.0 INTRODUCTION:
This chapter provides the background and context of the research problems, reviews the existing literature on the Artificial Intelligence Mobile App For Identification Of Cassava Diseases In Farm, and acknowledges the contributions of scholars who have previously conducted similar research [REV4800] …
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