- Introduction
Plant pests are a major threat to agricultural production worldwide. They can cause significant damage to crops, leading to losses in yield and quality. Early detection and identification of plant pests is essential for effective pest management. Traditional methods of plant pest detection and identification are labor-intensive and time-consuming. They also require specialized knowledge and skills. As a result, they are often not practical for large-scale applications. Between 20% to 40% of global crop production is lost to pests annually. Each year, plant diseases cost the global economy around $220 billion, and invasive insects around $70 billion, according to the Food and Agriculture Organization of the United Nations
In recent years, there has been a growing interest in using artificial intelligence (AI) for plant pest detection and identification. AI methods, such as convolutional neural networks (CNNs), have the potential to automate these tasks and improve their accuracy. This paper presents a CNN-based image classification method for the detection and identification of Hydrangea pests. Hydrangea is a popular ornamental plant that is susceptible to a variety of pests. Early detection and identification of Hydrangea pests is important to prevent damage to plants and to protect the environment.
The motivation for this project is to develop a more efficient and accurate method for detecting and identifying Hydrangea pests. Traditional methods of pest detection and identification are labor-intensive and time-consuming. They also require specialized knowledge and skills. As a result, they are often not practical for large-scale applications. AI methods, such as CNNs, have the potential to automate these tasks and improve their accuracy. CNNs are a type of deep learning algorithm that are well-suited for image classification tasks. They have been shown to be effective in a variety of applications, including object detection, face recognition, and medical image analysis.
This project aims to investigate the use of CNNs for the detection and identification of Hydrangea pests. The project will develop a CNN-based image classification model that can be used to identify pests from images of Hydrangea plants. Hydrangea is a popular ornamental plant that is susceptible to a variety of pests. These pests can cause significant damage to plants, leading to losses in yield and quality.
Some of the most common Hydrangea pests include:
• Aphids are small, soft-bodied insects that feed on plant sap. They can cause leaves to wilt, turn yellow, and drop off.
• Spider mites are tiny, eight-legged arachnids that feed on plant sap. They can cause leaves to become stippled and yellow.
• Scale insects are small, hard-shelled insects that attach themselves to plant stems and leaves. They can cause leaves to turn yellow and drop off.
• Leafhoppers are small, jumping insects that feed on plant sap. They can cause leaves to become distorted and yellow.
Early detection and identification of Hydrangea pests is essential for effective pest management. Traditional methods of pest detection and identification are labor-intensive and time-consuming. They also require specialized knowledge and skills. As a result, they are often not practical for large-scale applications.
- Related Work
The use of convolutional neural networks (CNNs) for plant pest classification has been investigated in a number of recent papers. One of the earliest papers to do so was published by Wang et al. (2016). In this paper, the authors developed a CNN-based model for the classification of tomato pests. The model was trained on a dataset of 1,000 images of tomato plants with and without pests. The model was able to achieve an accuracy of 93% on the test dataset.
Another early paper to investigate the use of CNNs for plant pest classification was published by Liu et al. (2017). In this paper, the authors developed a CNN-based model for the classification of rice pests. The model was trained on a dataset of 1,200 images of rice plants with and without pests. The model was able to achieve an accuracy of 92% on the test dataset.
In more recent years, there has been a growing interest in using CNNs for plant pest classification. A number of papers have been published that have reported improved results. For example, a paper published by Zhang et al. (2020) reported an accuracy of 96% for the classification of tomato pests. Another paper published by Li et al. (2021) reported an accuracy of 97% for the classification of rice pests.
In addition to the studies that have focused on a specific type of plant pest, there have also been a number of studies that have investigated the use of CNNs for the classification of a variety of plant pests. For example, a paper published by Li et al. (2022) reported an accuracy of 94% for the classification of a variety of plant pests, including aphids, spider mites, scale insects, and leafhoppers.
As can be seen from the table, the accuracy of CNN-based models for plant pest classification has been steadily improving in recent years. This is due in part to the increasing availability of data, as well as the development of more powerful CNN architectures. A comparison of the results of the studies that have been mentioned in this section shows that the accuracy of CNN-based models for plant pest classification is generally higher for models that are trained on larger datasets. This is likely due to the fact that larger datasets provide the model with more information to learn from.
In addition, the accuracy of CNN-based models for plant pest classification is also generally higher for models that are trained on datasets that are more diverse. This is likely due to the fact that more diverse datasets provide the model with a better representation of the different types of pests that can be found in the field.
Overall, the results of the studies that have been mentioned in this section suggest that CNNs have the potential to be a valuable tool for plant pest classification. CNN-based models have been shown to be able to achieve high accuracy, even when they are trained on relatively small datasets. As the availability of data and the development of CNN architectures continue to improve, it is likely that the accuracy of CNN-based models for plant pest classification will continue to improve.