Accurate estimation of tuber size in large potato throughput at potato storage using machine vision and machine learning techniques

• Machine vision to replace industrial semi-manual grading of potato based on Mask R-CNN was developed. • The machine learning algorithm includes creating seven novel features to distinguish fully visible tubers in clusters. • Detection of fully visible tubers was achieved by implementing random forest, increasing sampling rate by at least 21 folds. • The hardware and software design enabled cost-effective field deployment without compromising accuracy of current practices. In this research work, a machine vision system to sample and measure the lengths of potato tubers was developed and deployed. The proposed system generates information about tuber sizes in real-time and replaces the current practice adopted by the potato industry, which involves costly and labor-intensive activities such as manually sampling potato tubers and passing them under a dedicated laser sensor. The machine vision system, which utilized an RGB camera and a mask region-based convolutional neural network (Mask R-CNN), was able to detect tubers on conveyors under a wide range of light conditions with a sampling accuracy of 90.74 % and a mean intersection over union (mIoU) of 93 %. This work also describes 7 novel features to characterize fully tubers detected by the Mask-R-CNN model. These features are used by a Random Forest model to sample at least one and up to five tubers in clusters that ranged between 54 and 231 tubers. The proposed system showed excellent performance in the detection and sampling of tubers, under both static and dynamic conditions, with a worst-case relative root mean square error (nRMSE) value in estimating the long and short diameters of 6.71 and 7.83 %, respectively, in the densely clustered scenarios. The system leverages a graphical processing unit (GPU), which allows the processing of one batch of tubers per second in field, leading to an increase of 21-fold in tuber sampling ability when compared to the current industrial practice, while eliminating the time and labor needed to semi-manually transport and measure tubers.