Papaver somniferum and Papaver rhoeas Classification Based on Visible Capsule Images Using a Modified MobileNetV3-Small Network with Transfer Learning

Traditional identification methods for Papaver somniferum and Papaver rhoeas (PSPR) consume much time and labor, require strict experimental conditions, and usually cause damage to the plant. This work presents a novel method for fast, accurate, and nondestructive identification of PSPR. First, to fill the gap in the PSPR dataset, we construct a PSPR visible capsule image dataset. Second, we propose a modified MobileNetV3-Small network with transfer learning, and we solve the problem of low classification accuracy and slow model convergence due to the small number of PSPR capsule image samples. Experimental results demonstrate that the modified MobileNetV3-Small is effective for fast, accurate, and nondestructive PSPR classification.

Automated summary

Traditional identification methods for Papaver somniferum and Papaver rhoeas (PSPR) are time-consuming, labor-intensive, require strict experimental conditions, and often lead to plant damage. This research introduces a new method for fast, accurate, and nondestructive identification of PSPR. By constructing a PSPR visible capsule image dataset and using a modified MobileNetV3-Small network with transfer learning, the problem of low classification accuracy and slow model convergence caused by limited PSPR samples is addressed. Experimental results demonstrate the effectiveness of the modified MobileNetV3-Small network for fast, accurate, and nondestructive PSPR classification.