A precise grape yield prediction method based on a modified DCNN model

Abstract

This paper presents an innovative method for predicting grape yield using a modified deep convolutional neural network (M−DCNN). This approach integrates advanced image processing techniques with deep learning algorithms to analyze high-resolution images of vineyards, enabling precise yield quantification. The M−DCNN model incorporates novel convolutional layers and activation functions to enhance accuracy and generalization capabilities. Extensive experiments were conducted using a varied dataset of grape varieties and vineyard conditions. The method involves image processing to identify grape clusters and monitor their growth at different stages. We evaluated the performance of several algorithms, including K-nearest neighbors (KNN), multinomial logistic regression (MLR), support vector machines (SVM), random forests (RF), deep convolutional neural network (DCNN), and M−DCNN, throughout the training and testing phases. The M−DCNN achieved the highest training accuracy at 97.35%, which represents a 0.85% improvement over the standard DCNN. Furthermore, in detecting grape berries, our method attained an average F1 score of 73.32% and coefficients of determination exceeding 0.98, showcasing high accuracy and precision. The results indicate that the M−DCNN model surpasses traditional yield estimation methods and existing machine learning models, affirming its potential to significantly enhance precision agriculture. This method offers a robust tool for accurate grape yield forecasting, facilitating informed decision-making in viticulture.