Vegetable Classification and Analysis Based on the Improved CNN

Vegetable species identification is of great significance in fields such as computer science and artificial intelligence. It also has important application value in multiple fields such as agriculture, food science, and logistics. This research aims to develop a vegetable species recognition system based on convolutional neural network (CNN) to enhance the accuracy and efficiency of vegetable quality inspection which is of great help to in food production, agricultural management, market sales, catering, and scientific research. This research utilized an image dataset obtained from vegetable samples. Initially, the research preprocesses the image data, followed by the construction of a CNN. Finally, this research trains the model for image classification. The standard CNN model achieves an accuracy of 84.12%, while other improved CNN models achieves over 96% accuracy in vegetable species classification. The research in this paper holds significant importance. Firstly, vegetable recognition can facilitate vegetable quality assessment, inventory management, and supply chain optimization. Secondly, automated agriculture, smart retail, the restaurant industry, and food production all require vegetable species classification. Finally, it can also contributes to increasing vegetable yield and quality, reducing waste, and minimizing chemical pesticide usage.