Multi-growth stage plant recognition: a case study of Palmer amaranth (Amaranthus palmeri) in cotton (Gossypium hirsutum)
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.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionPlant PhenotypingSimilar Papers 제목 키워드 기반
Feature extraction for plant growth estimation
Precision agriculture requires the estimation of plant growth stages in real-time. When the plant growth stage is known, the wastage of resources in cultivation, such as nutrients and water, is reduced as only the requir…
Transfer LearningIdentification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches
Early identification of abnormalities in plants is an important task for ensuring proper growth and achieving high yields from crops. Precision agriculture can significantly benefit from modern computer vision tools to m…
Anomaly DetectionMachine Vision System for 3D Plant Phenotyping
Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis i…
3D ReconstructionPlant PhenotypingAbiotic Stress Prediction from RGB-T Images of Banana Plantlets
Prediction of stress conditions is important for monitoring plant growth stages, disease detection, and assessment of crop yields. Multi-modal data, acquired from a variety of sensors, offers diverse perspectives and is …
PredictionGenerative Plant Growth Simulation from Sequence-Informed Environmental Conditions
A plant growth simulation can be characterized as a reconstructed visual representation of a plant or plant system. The phenotypic characteristics and plant structures are controlled by the scene environment and other co…