Automatic Grading of Green Intensity in Soybean Seeds

In this work we introduce a low cost machine vision system for grading problems in agriculture. Instead of a careful evaluation of a given quantity over a reduced number of samples with a high cost dedi- cated equipment, we propose to measure the quantity with less precision but over a much bigger number of samples. The advantage of our proce- dure is that very low cost vision equipment can be used in this case. For example, we used a standard atbed scanner as an integrated illumina- tion plus acquisition hardware. Our system is aimed at the quantication of the amount of chlorophyll present in a production batch of soybean seeds. To this end we arbitrarily divided green seeds in four classes, with a decreasing amount of green pigment in each class. In particular, in this work we evaluate the possibility of an accurate discrimination among the four classes of green seeds using machine vision methods. We show that morphological features have low discrimination capabilities, and that a set of simple features measured over color distributions provides good separation among grades. Also, most errors are assignations to neighbor grades, which have a lower cost in grading. The good results are almost independent from the classier being use, Random forest or Support Vector Machines with a Gaussian kernel in our case.