Early Identification Of Weak Watermelon Seedlings Based On Phenotype Detection And Machine Learning

In factory nursery,it is important to make the correct decision of seedling division and replenishment at the seedling transplanting stage to improve the quality of seedlings and enhance the economic benefits.At present,the decision of seedling seeding replenishment operation in China is still based on manual experience,which is inefficient and subjective,and an intelligent detection technology is urgently needed.In this paper,we propose a method for phenotype detection,phenotype prediction and weak seedling discrimination of watermelon seedlings based on computer vision and machine learning technology.The main research contents and conclusions are as follows:1.A set of calibration methods for the Azure Kinect camera is proposed.To improve the accuracy of seedling phenotype measurement using the Azure Kinect camera,color and depth cameras were calibrated for watermelon seedlings with short shapes and high requirements for image acquisition equipment.The color camera was calibrated and the aberrations were corrected using the Zhang Zhengyou calibration method;the depth camera was calibrated and corrected using the depth image pixel correction method to establish a mapping relationship between the depth value and the true value.The experimental results show that the color image aberration is well corrected,the standard deviation of the depth information of the camera at 1m distance is 0.0535 before correction and 0.0056 after correction,the standard deviation is obviously reduced,the data is more moderate,the depth information aberration phenomenon is corrected to a certain extent,which provides hardware guarantee for the subsequent measurement of tiny watermelon potted seedlings.2.Experimental data such as images of watermelon seedlings,manually measured phenotypes,and strong seedling indices were collected for 8 consecutive days after the cotyledon spreading and flattening stage of the early Jia 84-24 variety.The Azure Kinect was used to collect data from the top view of the entire tray of watermelon seedlings three times a day at 9:00,14:00 and 19:00 for eight days,and the morphological index of each seedling was manually determined based on the robustness index equation on the eighth day of data collection.The large amount of experimental data provided data for the subsequent algorithm development and testing.Experimental data such as images of watermelon seedlings of early Jia 84-24 variety,cotyledon spreading stage after 8 consecutive days,manually measured phenotypes,and strong seedling index were collected.The Azure Kinect was used to capture the top view of the entire tray of watermelon seedlings three times a day,at 9:00,14:00 and 19:00,for eight days,and the morphological index of each seedling was manually determined based on the robustness index equation on the eighth day of data collection.The large amount of experimental data provided data for the subsequent algorithm development and testing.3.A nondestructive detection and prediction algorithm for plant height and leaf area of watermelon seedlings is proposed.The pre-processing,image segmentation and point cloud processing methods were used to locate each seedling and obtain plant height and leaf area data for each seedling.93.75%,87.5% and 87.5% of the recognition accuracy were obtained for hole hole identification of cavity tray seedlings on day 1,day 2 and day 3,respectively.In the phenotype detection experiment,the overall R2 of plant height and leaf area measurements were 0.901 and 0.922,respectively,and the predicted R2 of plant height and leaf area for the first three days using LSTM recurrent neural network were 0.952,0.942,0.932,0.923,and 0.866 for plant height,t+1,t+2,t+3,t+4,and t+5,respectively.The R2 for leaf area at t+1,t+2,t+3,t+4,and t+5 were0.926,0.919,0.901,0.882,and 0.862,respectively.t+3 was chosen as the step model to ensure the prediction accuracy and to meet the desired early prediction needs of the nursery plant.4.Based on the phenotypic prediction data and the manually calculated strong seedling index,a weak seedling identification model was established using multiple classifiers.Six machine learning classification methods,including random forest,SVM,and XGBoost,were used to discriminate normal seedlings(strong seedlings)and abnormal seedlings(weak seedlings)dichotomously on the last day of data,and the experimental results showed that the joint predictive classification model of LSTM and random forest had the highest classification accuracy of 89.9%,with good classification accuracy.5.A human-machine software based on PyQt5 was developed and integrated with watermelon seedling phenotype measurement algorithm and weak seedling early identification algorithm.The software has the functions of watermelon seedling cavity identification,single seedling location,seedling phenotype detection,and strong and weak seedling identification.The user only needs to input the image of the whole tray of watermelon seedlings,and the software system can give the algorithm corresponding results and decisions.The human-machine interface is friendly,simple and easy to operate.This paper establishes a high-precision and low-cost model for early discrimination of weak watermelon seedlings by non-destructive measurement and prediction of watermelon seedling phenotype and classification of strong seedlings,and integrates computer vision methods and various machine learning methods.The research can provide some vision technical support and decision basis for the realization of intelligent unmanned seedling plant and unmanned operation of transplanting robot in seedling plant,and has good promotion application value.