Moisture Stress Detection in Soybean Crops Using Sentinel-2 Time-Series NDVI and Machine Learning Techniques - Rahul B. Mannade (International Journal of Intelligent Systems and Applications in Engineering, 2021)
The measurement of moisture stress in crops is necessary in enhancing agricultural productivity as well as making water resource management sustainable especially in rainfed agricultural systems. Conventional field-based approaches tend to be restricted in spatial and temporal terms, and remote sensing could be considered as an option to monitor crops on a large scale. This paper introduces a machine learning-based model of moisture stress detection in soybean plants with the help of time-series satellite data. Sentinel-2 multispectral imagery was used in the growing season. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated using monthly composite images to calculate vegetation and moisture indices respectively. The dataset was in pixel format with moisture stress labels determined based on NDWI values that were determined by a dynamic threshold method. To prevent leaking of data and provide model reliability, NDVI time-series characteristics were taken as input variables, whereas NDWI was applied only to label generation. A Random Forest model was used to estimate the connection between the vegetation dynamics and the moisture stress situation. The overall accuracy of the model was 93.33, which means that the model has a high predictive power. The analysis of the importance of features showed that the values of NDVI during the later growth stages and especially in September and October were the most significant to detect the stress, and crop monitoring over time is important. The findings indicate that vegetation indices are useful to monitor the patterns of moisture stress without necessarily using water-related inputs. This research establishes the possibility of combining remote sensing and machine-learning methods in an efficient and scalable crop stress monitoring. The suggested solution offers an economical solution in making agricultural decisions and can be generalized to other crops and area to apply precision farming.