The agriculture sector plays a very important role in increasing population year by year to fulfill their requirements and contributes significantly to the economies of country. One of the main challenges in agriculture is the prevention and early detection of pest attack in crops. Farmers spend a significant amount of time and money in detecting pest and disease, often by looking at plant leaves and analyzing the presence of diseases and pests. Late detection of pest attacks and improper use of pesticides application, which can cause damage to plant and compromise food quality. This problem can be solving through artificial intelligence, machine learning, and accurate image classification system. In recent years, the machine learning has made improvement in the recognition of image and classification. Hence, in this research article, we used convolutional neural network (CNN)- based models, such as Cov2D library and VGG-16, to identify pest attacks. Our experiments involved a personal dataset consisting of 7000 images of pest attacked leaf samples of different position on maize plants, categorized into two classes. The Google Colab environment was used for experimentation and implementation, specially designed for cloud computing and machine learning.