Sustainable agricultural decision-making using machine learning and deep learning techniques : an approach to crop and pesticide recommendation with uncertainty quantification | |
| Author | Alam, Md. Sakib Bin |
| Call Number | AIT Thesis no.DSAI-24-02 |
| Subject(s) | Agriculture--Decision making Precision farming--Technological innovations Deep learning (Machine learning) |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence |
| Publisher | Asian Institute of Technology |
| Abstract | Precision agriculture supported by cutting-edge technologies, has emerged as a revolutionizing concept to optimize farming processes. The study examines the application of machine learning and deep learning techniques within the precision farming platform, focusing on crop and pesticide recommendation models. Previous studies have already demonstrated the beneficial potential of such technology in helping farmers make more informed decisions. However, one area of concern has been largely omitted in this context: uncertainty quantification in machine learning models. Indeed, real-world agriculture systems are uncertain or not easily predictable due to unpredictable environmental conditions and the complexity of interactions between numerous factors. The proposed study addresses this gap by introducing uncertainty quantification into crop and pesticide recommendation models, aiming to enhance robustness and reliability. The research not only seeks to improve the state-of-the-art results in experimented datasets but also contributes to the development of adaptive and resilient agricultural practices, fostering sustainable and efficient farming.The main objectives include the development of ML and DL models, incorporation of uncertainty quantification techniques, and an extensive evaluation of their performance, ultimately advancing agricultural decision-making for a more resilient and sustainable future. The experimental results demonstrate the superior performance of our ensemble model for crop recommendation, achieving an accuracy of 99.54%, surpassing existing studies. Furthermore, our developed Resnet152 model surpassed previous pest detection models, attaining an accuracy of 99.06%. Additionally, this study delves into the significance of uncertainty in ML/DL models through various ablation studies. A web application is also developed to demonstrate the usage of the proposed recommendation models. |
| Year | 2024 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Vatcharaporn Esichaikul (Co-chairperson);Chutiporn Anutariya (Co-chairperson) |
| Examination Committee(s) | Chantri Polprasert;Attaphongse Taparugssanagorn |
| Scholarship Donor(s) | His Majesty the Kings Scholarships (Thailand) |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2024 |