Advancing Plant Identification Through Leaf Image Analysis: A Comprehensive Review of Literature and Techniques

Advent of the machine learning and computer vision practices has enabled significant growth in the arena of plant prediction from leaf images. The ability to accurately predict plant species and health status based on leaf characteristics has numerous applications in agriculture, environmental monitoring, and biodiversity conservation. This study mainly concentrates on exploring the recent advancements in machine learning for plant prediction using leaf images. This paper begins by discussing the importance of plant prediction and the challenges associated with it. Various image acquisition techniques and datasets commonly used in this domain are presented, highlighting their strengths and limitations. Subsequently, a comprehensive analysis of the various phases of plant prediction, including leaf image preprocessing, feature extraction, and classification is provided. Key techniques and algorithms utilized in each stage are discussed, ranging from traditional computer vision methods to deep learning approaches. The survey then delves into the evaluation metrics and benchmark datasets commonly employed to assess the performance of plant prediction models. Comparative analyses of existing studies are presented, highlighting their strengths and limitations. Furthermore, challenges and open research problems in accurate plant prediction are identified, such as the need for larger and more diverse datasets, interpretability of deep learning models, and robustness to varying environmental conditions. At the end, this survey concludes with a discussion of potential future research directions and emerging trends in the field. These include the integration of multi-modal data sources, such as hyper-spectral and thermal imaging, the application of transfer learning techniques for domain adaptation, and the use of explainable AI methods to enhance model interpretability and trustworthiness. Indeed, this survey paper serves as a comprehensive guide for researchers, practitioners, and enthusiasts interested in accurate plant prediction using leaf images. It synthesizes the existing literature, delivers insights into the strengths and limitations of current methods, and outlines potential avenues for further research, ultimately contributing to the advancement of this important field.