Muthukumar Bagavathiannan

Guy RY Coleman, Matthew Kutugata, Michael J Walsh et al.

Many advanced, image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but their ability to recognize growth stages, often with stark differences in appearance, is uncertain. Amaranthus palmeri (Palmer amaranth) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production, exhibiting highly variable plant morphology both across growth stages over a growing season, as well as between plants at a given growth stage due to high genetic diversity. In this paper, we investigate eight-class growth stage recognition of A. palmeri in cotton as a challenging model for You Only Look Once (YOLO) architectures. We compare 26 different architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 on an eight-class growth stage dataset of A. palmeri. The highest mAP@[0.5:0.95] for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar growth stages. With all growth stages grouped as a single class, performance increased, with a maximum mean average precision (mAP@[0.5:0.95]) of 67.05% achieved by v7-Original. Single class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures.

Shuangyu Xie, Chengsong Hu, Muthukumar Bagavathiannan et al.

To enable robotic weed control, we develop algorithms to detect nutsedge weed from bermudagrass turf. Due to the similarity between the weed and the background turf, manual data labeling is expensive and error-prone. Consequently, directly applying deep learning methods for object detection cannot generate satisfactory results. Building on an instance detection approach (i.e. Mask R-CNN), we combine synthetic data with raw data to train the network. We propose an algorithm to generate high fidelity synthetic data, adopting different levels of annotations to reduce labeling cost. Moreover, we construct a nutsedge skeleton-based probabilistic map (NSPM) as the neural network input to reduce the reliance on pixel-wise precise labeling. We also modify loss function from cross entropy to Kullback-Leibler divergence which accommodates uncertainty in the labeling process. We implement the proposed algorithm and compare it with both Faster R-CNN and Mask R-CNN. The results show that our design can effectively overcome the impact of imprecise and insufficient training sample issues and significantly outperform the Faster R-CNN counterpart with a false negative rate of only 0.4%. In particular, our approach also reduces labeling time by 95% while achieving better performance if comparing with the original Mask R-CNN approach.