Early Identification and Localization Algorithm for Weak Seedlings Based on Phenotype Detection and Machine Learning

Abstract

It is important to propose the correct decision for culling and replenishing seedlings in factory seedling nurseries to improve the quality of seedlings and save resources. To solve the problems of inefficiency and subjectivity of the existing traditional manual culling and replenishment of seeds, this paper proposes an automatic method to discriminate the early growth condition of seedlings. Taking watermelon plug seedlings as an example, Azure Kinect was used to collect data of its top view three times a day, at 9:00, 14:00, and 19:00. The data were collected from the time of germination to the time of main leaf growth, and the seedlings were manually determined to be strong or weak on the last day of collection. Pre-processing, image segmentation, and point cloud processing methods were performed on the collected data to obtain the plant height and leaf area of each seedling. The plant height and leaf area on the sixth day were predicted using an LSTM recurrent neural network for the first three days. The R squared for plant height and leaf area prediction were 0.932 and 0.901, respectively. The dichotomous classification of normal and abnormal seedlings was performed using six machine learning classification methods, such as random forest, SVM, and XGBoost, for day six data. The experimental results proved that random forest had the highest classification accuracy of 84%. Finally, the appropriate culling and replenishment decisions are given based on the classification results. This method can provide some technical support and a theoretical basis for factory seedling nurseries and transplanting robots.

Design, Integration, and Experiment of Transplanting Robot for Early Plug Tray Seedling in a Plant Factory

“…Subsequently, statistical analysis is conducted on the pixel intensity peaks along the horizontal and vertical directions to determine the plug tray boundary. This boundary information is then utilized to calculate the centroid, which aids in precise positioning of the individual cell within the plug tray [13,36]. This methodʹs positioning accuracy depends on the contourʹs extraction effect..…”

“…However, the development of plant factories is constrained by drawbacks such as high energy consumption [9,10], high per-unit area costs [11], and limited space, which is not conducive to the installation of large-scale automated equipment [10]. Moreover, the suboptimal quality of the seeds, imprecise seeding within the plug trays, unfavorable temperature and humidity conditions, and insufficient nutritional provision, along with the presence of plant diseases and pests, collectively contribute to a suboptimal germination rate ranging between 80% and 95% [12,13] for seedlings cultivated in plug trays [14] and subsequently lead to yield losses in both the per-unit energy consumption and per-unit area. This, in turn, exacerbates the operational costs of plant factories.…”

“…Tong [28,29] employed the watershed segmentation algorithm to partition leaves and subsequently computed the area and perimeter of individual leaves within tray seedlings. Xu [13] designed a visual system for the early differentiation of underdeveloped watermelon seedlings, utilizing phenotype detection and machine learning. This system relied on two early characteristics, seedling height and leaf area, to evaluate their growth status, resulting in an 84% discrimination accuracy.…”

Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning

“…The leaf number regression task and the leaf area regression task apply the ReLU activation function in the task-specific fully connected layer to obtain the final prediction results. The ReLU function can overcome the problem of gradient disappearance, and is calculated using Equation (2).…”

“…Plant phenotypes are the physical, physiological, and biochemical characteristics of plants that reflect their structure and composition. Traditional phenotype analysis is performed manually, which introduces problems such as high labor and material costs, and low efficiency [1,2]. Therefore, traditional methods are unable to meet the demands of high-throughput plant phenotype research.…”