Machine Learning Approaches for Rice Seedling Growth Stages Detection - Tan, Suiyan; Liu, Jingbin; Lu, Henghui; Lan, Maoyang; Yu, Jie; Liao, Guanzhong; Wang, Yuwei; Li, Zehua; Qi, Long; Ma, Xu (Frontiers in Plant Science, 2022)
Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Traditional machine learning algorithms and deep learning algorithms were both investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at three meters height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOG) were selected as texture features, and combined with support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9%, 85.9%, 84.9% and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficeintnet family and other state-of-art CNN models (VGG16, Resnet50, Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47%, 99.53%, 99.39%, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tools to detect rice seedling growth stages.