Advancements in Artificial Intelligence (AI) for enhanced insights and automation in rice agriculture: A systematic review

Abstract

With the rising global demand for rice, improving production efficiency through advanced technologies like artificial intelligence (AI) is crucial. This systematic review gathered recent literatures on learning algorithm models applied to automate rice agriculture tasks. The objectives were to analyze the performance accuracy of different machine learning algorithms for rice classification and determine the most effective models. The 116 studies from 2016-2023 were screened and 70 were included. The algorithms were evaluated by weighted mean accuracy percentage across studies while maintaining consideration to sample sizes. The results showed the DenseNet121 deep convolutional neural network achieved the overall highest accuracy of 99.98%, also topping rice disease detection. For variety classification, Deep Neural Networks reached 99.95% accuracy by learning complex visual differences. Adaptive Neuro-Fuzzy Inference System led in grading quality of 98.6% by discerning grain features. Larger datasets improved accuracy indicating that the more training data has, it enhances model accuracy. The review demonstrates AI’s significant potential to automate essential aspects of rice production. Further research expanding standardized algorithm evaluations is recommended to strengthen the evidence-base and support integration of AI for intelligent, sustainable rice agriculture.