Humanity is heading towards a crisis. The mammoth task of providing food for 2 billion more people by 2050 is being deliberated by governments, scientists and agriculturists alike. The consequence of climate change has led to erratic and non-uniform crop growth. Floods are inundating agriculture land, drought is making crop cultivation impossible and pests and insects are wiping out entire crop fields. Just as the situation seems to be going out of hand, technology is proving itself to be the guardian angel yet again. With the power of Machine Learning and Artificial Intelligence, scientists are able to understand and predict intolerable growing conditions, identify various weather and pest infestation patterns and provide sustainable solutions, which helps accomplish our ultimate goal—increasing crop production by two-folds in the next thirty years. This paper gives an insight about ways in which artificial intelligence and machine learning are helping humanity overcome one of its biggest challenge.

Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user’s behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIPBSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIPBSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods.