Plant Gene and Trait 2024, Vol.15, No.1, 1-7 http://genbreedpublisher.com/index.php/pgt 4 3.2 The results and enlightenment of model experiment The model experiment has obtained many achievements and enlightenment in exploring the response mechanism of wheat to drought. These experiments provide an opportunity to gain insight into the mechanisms and characteristics of wheat drought response. Through model experiments, researchers are able to identify and understand the physiological, molecular level changes in wheat under drought conditions and its response to environmental stress. This provides us with methods and strategies for improving drought resistance of wheat in agricultural production (Ahmed and Hussain, 2022). The model experiments also revealed the effects of drought on wheat growth and yield. By simulating and predicting the growth status and yield changes of wheat under different drought conditions, we can better assess the impact of drought on wheat planting yield, and provide scientific basis and advice for wheat planting under drought conditions. The model experiments also provide tools and methods for predicting and evaluating wheat response to drought. By building machine learning models, the researchers were able to predict wheat growth, yield changes and its response to drought stress in different drought scenarios. This provides an important reference for wheat variety improvement and agricultural management in the future. In the field of soybean research, model experiments have helped to identify the factors that affect the yield and quality of soybean under different growth conditions. Based on the analysis of climate, soil, plant characteristics and other data, the model can accurately predict soybean growth and yield changes, which helps farmers optimize land management and planting methods, and improve soybean yield and quality. The model experiments also provided insights into soybean diseases and pests. The model can identify common soybean diseases and insect pests, and predict their spread path and impact degree. This prediction facilitates the early implementation of necessary control measures to protect soybean crops from diseases and pests. Model experiments also play an important role in soybean breeding. Through the analysis of genomics and epigenomics, the model can more comprehensively understand the genetic characteristics and growth patterns of soybean varieties, and provide more accurate data support for seed selection and breeding. 3.3 Application of machine learning model to wheat drought response Machine learning model plays an important role in the study of wheat drought response. A team of researchers built a convolutional neural network model by collecting physiological data and environmental parameters (such as soil moisture, air temperature, humidity, etc.) of wheat at different growth stages during the experiment. The model can predict the growth state of wheat under different drought levels. The research team processed and labeled the data set, and then designed a deep convolutional neural network to optimize the model through training and validation to improve the prediction accuracy. Through the application of convolutional neural network, the research team can more accurately predict the growth of wheat under drought conditions. This case shows us the application prospect of advanced machine learning technology in the agricultural field. Using deep learning models such as convolutional neural network to solve agricultural problems not only improves the efficiency of agricultural production, but also improves the efficiency of agricultural production. It also promotes the innovative application of science and technology in the field of agriculture. By processing a large amount of data, the machine learning model can accurately identify and predict the growth situation and yield changes of wheat under drought conditions. Through the analysis of multi-dimensional data such as environmental data, genetic information and growth indicators, the machine learning model can identify the key factors affecting wheat resistance to drought. This provides beneficial decision support for agricultural production (Ji and Li, 2019, Journal of Tonghua Normal University, 40(6): 73-77). Through the predictive power of the model, agricultural practitioners can better plan planting strategies, select wheat varieties adapted to drought conditions, and develop effective agricultural management practices to minimize the impact of drought on wheat yields.
RkJQdWJsaXNoZXIy MjQ4ODYzMg==