This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ech T Press Science Computers, Materials & Continua DOI: 10.32604/cmc.2023.030924 Article Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining Ahmed Abdu Alattab 1, *, Mohammed Eid Ibrahim 1 , Reyazur Rashid Irshad 1 , Anwar Ali Yahya 2 and Amin A. Al-Awady 3 1 Department of Computer Science, College of Science and Arts, Sharurah, Najran University, Najran, Saudi Arabia 2 Department of Computer Science, College of Computer Science & Information Systems, Najran University, Najran, Saudi Arabia 3 Computer Skills Department, Deanship of Preparatory Year, Najran University, Najran, Saudi Arabia *Corresponding Author: Ahmed Abdu Alattab. Email: aaalattab@nu.edu.sa Received: 06 April 2022; Accepted: 26 May 2022 Abstract: This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated repre- sentation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy. Keywords: Machine learning; agriculture; IoT; HLSTM; fuzzy rules 1 Introduction Farming is undergoing a revolution field to the latest technologies that appear to be highly promising because it allows the key sector to reach new levels of farm profitability and productivity. Agriculture is involving precision affecting inputs when it needed good farming. The 3 rd wave of the current agriculture revolution is being enhanced by the availability of larger amounts of data. It is difficult for farmers to know what crops are most suited as per their soil’s quality, nutrients, and structure. In this study, a data mining technique for soil quality analysis is proposed to assist farmers. Thus, the method reduces efforts on monitoring the soil quality to anticipate the crop suited for cultivation based on the soil type and to optimize crop production. This research concentrates on the