Predicting Winter Wheat Emergence and Stem Elongation Time using CNN

Abstract

Accurately identifying the growth stages of winter wheat is essential for effective crop management. The timing of each growth stage depends on various factors, including the sowing date, the type of winter wheat, and unpredictable factors such as precipitation, solar radiation, and temperature. Estimating the start of each stage can be challenging. However, deep learning models have proven to be effective in many agriculture-related applications, such as yield prediction, disease control, and nutrient recommendation. This study proposes the use of a convolutional neural network (CNN) model that can predict the emergence and stem elongation time of winter wheat by identifying tiny color changes. The model is trained on satellite images captured over a 3-year period. The proposed CNN model can achieve a prediction accuracy of up to 93.75% for stem elongation stage time and up to 91.75% for winter wheat emergence stage time. The proposed model outperforms other machine learning methods, such as SVM and Random Forest used for similar tasks in previous studies. The grayscale index is used as the input of the machine learning models. It can achieve 70% to 74.5% accuracy in the prediction of emergence and 82.5% to 84.4% accuracy in the prediction of stem elongation.