Harvesting Period Prediction Based on Stages of Mango Ripening Using CNN over Decision Tree

Machine learning is the study of computer algorithms that get better on their own as a result of usage of information and experience. It is seen as a neighbourhood of AI. The aim is to find accuracy in predicting the harvesting stages using Convolutional Neural Networks algorithm compared with a Decision Tree algorithm. This study contains a total of 2 groups, the convolutional neural network algorithm is analysed in the group 1 (10 samples), and the Decision Tree in the group 2 (10 samples). The accuracy and efficiency of each of the models are compared. This paper is to improve the efficiency of the algorithm used in the harvesting stages. The proposed model appears to be efficient and faster than the existing algorithm and the mean accuracy of detection is ±1SD. The final outcome of the existing system, Convolutional Neural Networks algorithm, is compared with the outcome of the novel Decision Tree algorithm and the proposed model proved to be having higher efficiency than the existing model.