Key points are not available for this paper at this time.
Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination of remote sensing technology and machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive and systematic reviews on the integrated application of these two technologies. For this reason, this study conducts a systematic literature search using the Web of Science, Scopus, Google Scholar, and PubMed databases and analyzes the integrated application of remote sensing technology and machine learning algorithms in PA over the last 10 years. The study found that: (1) because of their varied characteristics, different types of remote sensing data exhibit significant differences in meeting the needs of PA, in which hyperspectral remote sensing is the most widely used method, accounting for more than 30% of the results. The application of UAV remote sensing offers the greatest potential, accounting for about 24% of data, and showing an upward trend. (2) Machine learning algorithms displays obvious advantages in promoting the development of PA, in which the support vector machine algorithm is the most widely used method, accounting for more than 20%, followed by random forest algorithm, accounting for about 18% of the methods used. In addition, this study also discusses the main challenges faced currently, such as the difficult problems regarding the acquisition and processing of high-quality remote sensing data, model interpretation, and generalization ability, and considers future development trends, such as promoting agricultural intelligence and automation, strengthening international cooperation and sharing, and the sustainable transformation of achievements. In summary, this study can provide new ideas and references for remote sensing combined with machine learning to promote the development of PA.
Wang et al. (Sun,) studied this question.