Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera. Data from an online autonomous greenhouse challenge (Wageningen University, June 2021) were employed in this study. The data were collected using an Intel RealSense D415 camera. The developed model has a two-stage CNN architecture based on ResNet50V2 layers. The developed model provided coefficients of determination from 0.88 to 0.95, with normalized root mean square errors of 6.09%, 6.30%, 7.65%, 7.92%, and 5.62% for fresh weight, dry weight, height, diameter, and leaf area, respectively, on unknown lettuce images. Using red, green, blue (RGB) and depth data employed in the CNN improved the determination accuracy for all five lettuce growth indices due to the ability of the stereo camera to extract height information on lettuce. The average time for processing each lettuce image using the developed CNN model run on a Jetson SUB mini-PC with a Jetson Xavier NX was 0.83 s, indicating the potential for the model in fast real-time sensing of lettuce growth indices.

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HighlightsAutonomous rice transplanters often deviate during the transition between turning and straight paths.Headland turning path planning algorithms were designed to reduce transition distances before and after turning.Transition distances were predicted based on lateral deviations and heading errors over a threshold using simulations.The developed algorithms were validated for reducing transition distance with an auto-guidance system in a paddy field.Abstract. In the straight sections before and after headland turning in paddy fields, high lateral deviations and heading errors may occur due to changes in the path curvature and dynamic factors of the vehicle and path tracking algorithm. In the case of autonomous rice transplanters, such errors may lengthen the transitions between turning and straight sections and deteriorate working efficiency. In this study, headland turning path generation algorithms for rice transplanters were proposed to reduce the transition distances. Path models were defined considering the transition distances to move the vehicle such that the error was within the acceptable range. The transition distances were predicted by using a simulator that employed a kinematic vehicle model and a tracking algorithm with a model-based control system for generating steering angles. A look-ahead search method and tilt compensation for the Global Navigation Satellite System were applied as well. The developed method was validated in a paddy field by using a Daedong DRP60D rice transplanter. The transition distance decreased by 34.04% compared to that associated with the baseline path that did not consider the transitions. The overlap area decreased by an average of 55.51% within the straight lines. During straight operation, the maximum lateral deviation did not exceed 15 cm. These results confirmed that the proposed algorithms for autonomous rice transplanters can traverse a target path more accurately before and after turning and, consequently, increase work efficiency by avoiding non-planted areas. Keywords: Auto-guidance, Headland turning, Path planning, Prediction, Rice transplanter, Transition.