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Past studies of plant disease recognition used classification methods that presented a singular recognition result to the user. Unfortunately, incorrect recognition results may be output, which may lead to further crop damage. To address this issue, there is a need for a system that suggest several candidate results and allow the user to make the final decision. In this study, we propose a method for diagnosing plant diseases and identifying pests using deep features based on transfer learning (machine language). To extract deep features, we employ pre-trained VGG and ResNet 50 architectures based on the ImageNet dataset, and output disease and pest images similar to a query image via a k-nearest-neighbor algorithm. In this study, we use a total of 23,868 images of 19 types of hot-pepper diseases, for which, the proposed model achieves accuracies of 96.02 and 99.61%, respectively. We also measure the effects of fine- tuning and distance metrics. The results show that the use of fine-tuning-based deep features increases accuracy by approximately 0.7–7.38%, and the Bray–Curtis distance achieves an accuracy of approximately 0.65–1.51% higher than the Euclidean distance.

TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
CHAPTER ONE
INTRODUCTION

1. Background of the study
2. Statement of the problem
3. Aim and objectives of the study
4. Significance of the study

CHAPTER TWO
LITERATURE REVIEW

1. Review of the study
2. Related work
3. Hot pepper (chiles)

CHAPTER THREE
MATERIALS AND METHODS

1. Dataset description
2. Pre-trained models
3. Transfer learning of deep convolutional neural network
4. Visual geometry group model
5. Resnet model
6. Feature extraction and fine-tuning
7. K-nearest-neighbor  algorithm
8. Deep-learning methodologies
9. Proposed Method

CHAPTER FOUR
4.0 EXPERIMENTAL RESULTS
4.1      Tools and Setup
4.2      Measurement Criteria
4.3     Result
4.4     Discussion

CHAPTER FIVE

1. Conclusion
2. Recommendation

References
CHAPTER ONE
1.0                                                        INTRODUCTION
1.1                                           BACKGROUND OF THE STUDY
Hot peppers comprise one of the world’s most popular crops. In 2018, the Food and Agricultural Organization reported that production of hot-pepper (item: “chiles and pepper, green”) had steadily increased to approximately 36.8-million tons, up more than 14.4% compared with 2014 (Food and Agriculture Organization , 2018). Hot-pepper production is greatly affected by climate change (Aji et al., 2020), and owing to increased importing and exporting, the influx of foreign diseases are prominent threats.
Past studies of plant disease detection used classification methods that presented a singular recognition result to the user. Unfortunately, incorrect recognition results may be output, which may lead to further crop damage. Therefore, there is a need for a system that can offer multiple candidate results so that the user can intervene and weigh options. Google (2020) presents several candidate results to an image query and allows the final selection to be made by the user. The content-based image retrieval (CBIR) technique can also be used for this purpose. CBIR extracts features by applying a specific content (e.g., color and edge) descriptor to an image, and it outputs the most similar images to a query image using similarity comparison between features. However, owing to limitations of the feature-extraction descriptor, the recognition accuracy of diseases is low at 75–83% (Piao et al., 2017). Thus, it is necessary to improve recognition performance using a deep learning algorithm.
In cases where there are insufficient data, or models are not well-trained, transfer learning can be used (Kaya et al., 2019; Deng et al., 2020; Zhuang et al., 2021). Many studies on machine vision have employed transfer learning. It has been widely applied to solve problems related to image recognition using convolutional   neural   network (CNN) models. Typically, copious data, time dimensions, and computing resources   are   required   to   train   models with deep layers. An example is the visual geometry group (VGG) (Simonyan and Zisserman, 2014) and ResNet (He et al., 2016) models. These architectures have already shown excellent image verification performance with various large public datasets.
Transfer learning is a machine-learning methodology that focuses on knowledge transfer between domains. It can be quickly applied to tasks using pre-trained knowledge (Tsiakmaki et al., 2020; Zhuang et al., 2021). Thus, the number of cases using transfer learning to recognize diseases is increasing.
In this study, we propose an improved method for diagnosing diseases using transfer learning (machine language).

1.2     STATEMENT OF THE PROBLEM
Detecting plant leaf diseases is one of the major challenges faced by farmers in agriculture. It is very important to identify the type of leaf diseases accurately for appropriate use of pesticides. Any mistakes in identifying diseases of plants leads to reduced yield. Plant diseases can be either biotic (Balakrishna et al., 2020) or abiotic. Primary cause behind the biotic diseases are various living organisms like bacteria, virus, and fungi. Biotic diseases are affected by viruses unlike abiotic diseases which are affected by inorganic conditions like weather changes, chemicals etc. Identifying leaf diseases accurately by observing with naked eye is a difficult task. Hence, there is a requirement of an application that can detect leaf diseases accurately. There are various automated applications to identify plant leaf diseases. Most of them used texture representations extracted from leaf images with conventional Machine Learning models (Mannepalli et al., 2017). The study uses Deep learning with convolutional neural networks (CNNs) to detect hot pepper disease. Machine learning research presently perform efficient detection of plant disease from raw images. Currently, deep learning methods are performing better in literature especially in image processing.

1.3      AIM AND OBJECTIVES OF THE STUDY
The main aim of this study is to carry out a study on detecting hot pepper diseases using machine learning. The objectives of the study are:

1. To ensure healthy pepper leaves and bacterial pepper leaves using transfer learning (machine language).
2. To provide an early stage of hot pepper disease detection
3. To improve the quantity and quality of agricultural goods
4. To ensure development of the quality of the crop, and it reduces the damage for the production.

1.4      SIGNIFICANCE OF THE STUDY
The use of machine language in detecting hot pepper disease serves as an easier means of ensuring effective health screening in plants. In this way, the damage caused by diseases can be reduced by early detection. 

CHAPTER FIVE
5.1      CONCLUSION
In this study, we proposed an improved method for diagnosing hot-pepper diseases using a fine-tuning-based transfer learning method. To extract deep features, we employed pre-trained VGG16, VGG19, and ResNet50 models based on the ImageNet dataset and output disease  images most similar to the query image using the kNN algorithm. We used image data of 19 types of hot-pepper diseases and pests, and the experimental results showed that accuracies of 96.02 and 99.61% were achieved for diseases and pests, respectively. We also measured the effects of fine-tuning and distance metrics. The measurement results showed that fine-tuning improved the accuracy by approximately 0.7–7.38%, and the Bray-Curtis distance achieved a higher accuracy of approximately 0.65– 1.51% than that of the Euclidean distance. Furthermore, when comparing the performance between the proposed model and the classification, they showed an accuracy performance of 97.87 and 97.88%, respectively. In summary, an expert user is expected to derive more accurate pest recognition results from the proposed model, which requires manual image cropping around the disease area.

5.2                                              RECOMMENDATION
In the future, we will automatic the image cropping and measure its effectiveness by applying the proposed model to other crops.

 

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