Identification of Best Sowing Patterns of Crop using Satellite Imagery and Deep Learning

Abstract

Rapid growths in population and climate change are resulting in food insecurity. Food security is more challenged in developing countries like Pakistan where conventional practices of crop monitoring are in place. The two aspects of crop monitoring which are crop identification and yield prediction are manual which are time-consuming and resource extensive thus resulting in delayed decision making. However, the Precision Agriculture applications for crops monitoring based on Remote Sensing data can help us to effectively monitor crops and increase production and aid decision making. This study acquires remotely sensed multispectral and multitemporal satellite imagery from Sentinel-2 mission satellites to extract spectral bands over different timestamps of the rice, wheat, and sugarcane crop season. These spectral bands along with the computed vegetation indices over the growing season of wheat and rice are used with the Long Short Term Memory network for the early identification of the crops. The study also attempted to identify the best sowing dates of wheat crops with multispectral and multitemporal data. Further, the significance of the temporal data and different combinations of the spectral bands were analyzed for crop identification of the small-sized fields and a comparison was made with the existing state of the art. The best combination of the spectral bands resulted in 99.76 % accuracy for crop identification. Further, the crops are also identified with 93.77 % accuracy within the first four weeks of their seeding. Our study also identified the sowing week of wheat crop with RMSE of 0.8. The crop identification accuracy with the proposed approach is suggestive of the applicability of the study for the automatic identification of crops on large scale