Anomalies detection and cause analysis of autumn crops in individual croplands using timeseries of Sentinel-2 satellite data (Case Study: Golestan province)

One way to ensure food security is to produce strategic agricultural products on a large scale using industrial methods. Managing large-scale farms consistently and cohesively is a challenging task that requires the utilization of modern technologies. Crop anomalies refer to uncommon and limited factors during agricultural production, leading to localized differentiation in the crop cultivation process. Factors contributing to crop anomalies in agriculture include imbalances in soil nutrients and fertilizers, grazing during crop growth, pests, variations in soil texture and slope in pastures, weed growth, and drought. Detecting and remediating factors limiting crop growth in vast agricultural lands is difficult and these issues are often noticed at harvest time. This article suggests a solution for continuously monitoring of large agricultural fields by analyzing the time series of Sentinel-2 satellite images. The effectiveness of this solution in detecting various anomalies of farms, in agrarian areas has been demonstrated by the results. The proposed solution offers features such as timely diagnosis, the ability to monitor the continuation of irregularities, and the measurement of compensatory measures' effectiveness. The method has successfully identified over five types of anomalies in the selected farms, achieving a detection accuracy of 95.60%.