Application of Convolutional Neural Networks in Detecting Cropping Intensity: An Attempt Based on Global Typical Samples

Accurate estimation of cropping intensity is crucial for agriculture production, land management, and food security. Traditional land surveys and remote sensing techniques are often constrained by time and space limitations, while deep learning, particularly Convolutional Neural Networks (CNN), offers new opportunities to address this challenge. In this paper, we explored the use of CNN for calculating cropping intensity. First, we collected multi-temporal satellite imagery, the Enhanced Vegetation Index (EVI) of MOD13Q1 as the database which spans various cropping growth stages. Subsequently, a CNN model was employed to learn features from these datasets to capture changes in crop growth. After training, the CNN model can identify and classify different cropping intensity, indicating the frequency and intensity of crop planting in different regions and periods. Our research findings suggest that CNN holds promise for cropping intensity estimation. It provides higher precision of cropping intensity of over 90%, enhancing the understanding of dynamic changes in cropland for decision-makers. Furthermore, CNN exhibits adaptability to diverse geographic environments and crop types, thereby enhancing its generalization capabilities. This approach holds significant implications for improved land management, agricultural production, and agricultural policy efficiency. By introducing deep learning techniques to crop planting intensity estimation, we offer novel tools and methods for sustainable agriculture and effective land management. Future research will further refine this approach to meet monitoring requirements in different regions and environmental conditions.