Development and field evaluation of a machine vision based in-season weed detection system for wild blueberry

Abstract

The wild blueberry crop requires the heavy application of agrochemicals for the proper crop yield and weed control. An integrated machine vision based weed detection system was developed to target goldenrod weed spot-specifically. Color co-occurrence matrices and statistical classifiers were used for the goldenrod detection. The linear and quadratic classifiers were developed using different reduced sub-sets of textural features. The classifiers were evaluated using accuracy, specificity, sensitivity, and false negative ratio at Laboratory scale. Performance of the developed weed detection system was also evaluated in two wild blueberry fields. The results indicated that quadratic classifier with DM-HSISD model showed the best performance at laboratory scale with the classification accuracies of 94.98% and 93.80% for training and testing datasets, respectively. Another quadratic classifier (with DM-HSI model) containing all 39 textural features was found to be the second best with the accuracies of 94.29% and 91.46% on training and test datasets, respectively. The linear classifiers didn’t perform well compare to their respective quadratic counter-parts. Field performance of the developed system equipped with the quadratic DM-HSISD classifier indicated no significant difference between variable rate (VR) and uniform application (UA) in terms of mean percentage area coverage (PAC) for the targeted goldenrod spots in both fields. However, a significant difference was observed between mean PAC of VR and UA applications for the non-targeted wild blueberry spots. The potential and actual chemical savings were in ranges between 46.71% and 74.83% and 30.12% and 60.58% depending on the weed and sprayed area, respectively. These results demonstrated that the developed weed detection system has potential for targeted application of agrochemicals to control goldenrod in wild blueberry fields.