Unlocking Machine Learning Algorithms for Bambooshoots.AI: Revolutionizing Agricultural Applications with Computer Science

Abstract

This research initiated a comprehensive investigation to determine the most suitable Machine Learning (ML) algorithms for bamboshoots.AI application. Convolutional neural networks (CNNs) and support vector machines (SVMs) were cited as two essential ML techniques in a comprehensive review of ten papers. To further assess these two algorithms in the context of the Bambooshoots.ai application, an experimental analysis was also carried out. The CNN model had been trained over 25 epochs with 8 batches of data per epoch, demonstrating a consistent increase in accuracy, and reached 97.94% by the end of training. In contrast, the SVM model provided an accuracy of approximately 57.14%. The experimental results indicated that the CNN model had been better at classifying bamboo shoot images, thus making it the preferred choice for the BambooShoots.AI application. The significant difference in accuracy between the two models, as well as the consistent performance of CNNs in image-based tasks, informed this decision. The results of this research were thrilling because they showed that machine learning could be used to help understand and manage bamboo shoots. This could have been a big help to farmers and could have led to new ways of doing things in agriculture.