A study on fruit classification using convolutional neural network for image recognition technology

Abstract

Artificial intelligence is being incorporated into more aspects of our daily lives. Agricultural production in China is at a critical juncture due to the rising fruit production and dwindling labor force, necessitating the adoption of mechanization and intelligent systems. Image processing is particularly suitable for this field. Image classification technologies in fruit categorization are examined in this research. The results of this study can help improve the development of intelligent, lightweight machinery for agricultural output. This, in turn, will enhance the efficiency of fruit cultivation, harvesting, and trading and alleviate labor constraints. Two popular convolutional neural network models, namely ResNet50 and MobileNetV2, were employed in this study. The study utilized two optimizers: SGD and Adam. The evaluation results revealed that the ResNet50 model, employing SGD optimization, achieved the highest accuracy of 95.57%. Despite its lower accuracy of 92.19%, the MobileNetV2 model demonstrates higher efficiency than ResNet50 due to its lower hardware requirements, rendering it suitable for operation on compact devices.