Identification of the White-Mold affected Soybean fields by using Multispectral Imageries, Spatial Autocorrelation and Support Vector Machine

Remote Sensing has been contributing to increase in agricultural production with Site-Specific Crop Management based on development of the Geospatial technologies. In particular, identifying the crop disease affected regions becomes one of the major issues. One of methodology to solve this problem is Machine Learning technology. In this paper, we implemented an effective White-Mold disease (Sclerotinia sclerotiorum) identification with Support Vector Machine (SVM) and evaluated the accuracy using Spatial Autocorrelation (SA). Our implementation steps included relevant imagery extraction using the Spectral Vegetation Indices (SVI), SVM for White-Mold region identification using SVIs, and discernment of crop disease clustering pattern using SA. The obtained accuracy of SVM is up to 70% that showed promising performance of White-Mold identification from the given field. SA could identify a clustering pattern from given dataset, but the p-value analysis indicates that it is not statistically significant to generalize the clustering pattern onto whole White-Mold region.