This article deeply analyzes the current situation and key technologies of cotton production in Xinjiang. From 2020 to 2024, the average total fiber yield, planting area, and unit yield of cotton in Xinjiang reached 5.296 million tons, 2.4644 million hectares, and 2149.72 kg/ha, respectively. Among them, the highest total yield, planting area, and unit yield were 5.686 million tons, 2.5061 million hectares, and 2322.8 kg/ha, respectively. Additionally, the cotton quality of Xinjiang is excellent, and the comprehensive mechanization operation coverage rate has reached 97.0%. The success of Xinjiang’s cotton industry is primarily attributed to the development and promotion of a series of cotton planting technologies with distinct regional characteristics and leading technological levels, including identifying the “early, short, and dense” cultivation mode as a technological breakthrough, integrating comprehensive prevention and control of pest, disease, weed, and natural disaster, water-saving through mulched drip irrigation, and standardized management of mechanically harvested cotton. These technologies fully demonstrate the high adaptability of local cotton production techniques to natural conditions. Through these advanced technologies, the unfavorable factors of relatively poor natural ecological conditions in Xinjiang’s cotton region have been overcome, and the goal of high and stable cotton yield, high quality, simple, and efficient, has been successfully achieved. Xinjiang cotton not only provides strong support for local economic and social development, but also offers a “China Xinjiang cotton cultivation application plan” for the high quality and sustainable development of global cotton, particularly in cotton production areas with similar natural ecological conditions as Xinjiang.

The identification of the film on cotton is of great significance for the improvement of cotton quality. Most of the existing technologies are dedicated to removing colored foreign fibers from cotton using photoelectric sorting methods. However, the current technologies are difficult to identify colorless transparent film, which becomes an obstacle for the harvest of high-quality cotton. In this paper, an intelligent identification method is proposed to identify the colorless and transparent film on cotton, based on short-wave near-infrared hyperspectral imaging and convolutional neural network (CNN). The algorithm includes black-and-white correction of hyperspectral images, hyperspectral data dimensionality reduction, CNN model training and testing. The key technology is that the features of the hyperspectral image data are degraded by the principal component analysis (PCA) to reduce the amount of computing time. The main innovation is that the colorless and transparent film on cotton can be accurately identified through a CNN with the performance of automatic feature extraction. The experimental results show that the proposed method can greatly improve the identification precision, compared with the traditional methods. After the simulation experiment, the method proposed in this paper has a recognition rate of 98.5% for film. After field testing, the selection rate of film is as high as 96.5%, which meets the actual production needs.