The frequency of insect attacks has been on the rise in recent times. One effective method for identifying and classifying insects is through the detection of these insects within images. This approach is relatively simple and allows for accurate categorization of insects based on the content of the image. Insect infestations can cause significant harm to crops, resulting in financial losses for farmers. However, identifying the specific category of an insect can sometimes be a challenging task. Different types of insects cause varying degrees of damage to different crops. The impact of these attacks depends on the type of insect and the specific crop being cultivated. Additionally, insect attacks tend to occur during different climatic conditions. It is important to note that climate change can exacerbate the likelihood of insect infestations. These attacks not only lead to economic losses but also result in reduced crop production. To address the challenge of identifying and classifying insects in video footage, a proposed research approach suggests the implementation of a Convolutional Neural Network (CNN) architecture. This architecture aims to provide an effective solution by leveraging the capabilities of deep learning algorithms. To enhance the accuracy of the model, a confusion matrix can be utilized, allowing for the evaluation and refinement of the results. Overall, by employing advanced technologies such as CNNs and leveraging tools like the confusion matrix, it is possible to improve the identification and classification of insects in videos. This research has the potential to mitigate the economic damage caused by insect attacks and contribute to higher crop yields.

Insect detection, insect classification, Convolutional neural network, video processing, confusion matrix