# Crop Land Change Detection with MC&N-PSPNet
## 4 Discussion  
### Application Development, Directions, and Limitations  
The method in this paper will ensure the accuracy and efficiency of complete extraction of the basis of all agricultural areas, but the ability to screen out the visual interpretation of label loopholes, and then optimize the label. Through the results of change detection, it can also be seen that the method in this paper can detect the direction of change, and accurately obtain the bi-directional change of agricultural and non-agricultural areas. Based on this, future research will continue to optimize the dataset and the model according to different application directions, so that it can detect the specific change in agricultural land to another land type or detect contaminated soil. By comparing the recognition results with the recent literature [26], it is found that there are omissions in the recognition of the method in literature [26], and the edge smoothing is worse than the method of this paper. As far as accuracy is concerned, the overall accuracy of this paper's method is 1.17% higher. In terms of efficiency, the total training time of this paper's method is 0.31h faster. Regarding the application level of this paper's methodology, this has important potential benefits for agricultural practices, for example, in precision agriculture management. It accurately categorizes and monitors changes in agricultural zones, so that farmers and agricultural managers can more accurately understand changes in arable land and develop more effective planting and management strategies to improve crop yields and quality. In terms of resource optimization and allocation, the model can identify and monitor changes in agricultural zones to help agricultural managers to rationally allocate resources, such as water resources, fertilizers, and pesticides, to reduce waste and improve resource utilization efficiency. In disaster assessment and response, after natural disasters (e.g., floods, droughts), the model can quickly assess the impacts of disasters on agricultural zones, provide accurate change detection data, and support post-disaster recovery efforts and the implementation of disaster mitigation measures. In terms of environmental protection, by monitoring changes in agricultural areas, environmental degradation and soil erosion problems caused by irrational farming can be detected and prevented promptly, promoting sustainable agricultural development. In terms of policy formulation and planning, the government and relevant institutions can use the data provided by the model to formulate scientific agricultural policies and planning, promote the process of agricultural modernization, and enhance the overall competitiveness of agriculture. In summary, the MC&N-PSPNet model proposed in this paper not only technically realizes high-precision classification and change detection in agricultural areas, but also shows great potential and value in practical applications. However, practical applications still suffer from dataset limitations, model complexity, image quality, real-time processing capabilities, and adaptability to different domains. For example, detecting changes in cropping patterns or crop types is critical for accurate monitoring of agricultural areas. Different crops exhibit unique spectral characteristics and growth cycles that may not be fully captured by current datasets and model configurations. To improve model sensitivity to different crop types, future research will integrate multi-temporal and multi-spectral data. This approach can help to distinguish between various crops and their respective growth stages. In addition, combining auxiliary data sources such as crop growth cycles, soil moisture, and meteorological data can further improve the accuracy and reliability of the model in identifying and categorizing crop types. The ability of this paper's method to generalize across different agricultural landscapes and cropping practices is also a key aspect to consider.