Majlesi Journal of Electrical Engineering A Review on Application of Various Deep Learning Techniques and Filtering Approach in Plant Phenotyping Volume 18 (2024) Issue 1, March 2024 2024 04 21 A Review on Application of Various Deep Learning Techniques and Filtering Approach in Plant Phenotyping 10.30486/mjee.2024.1986435.1135 EN Rama devi Kalluri School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India 0000-0003-4010-2266 Prabha Selvaraj School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India 0000-0002-0820-9146 Journal Article 2024 04 21 Plant phenotyping is one of the recent research areas that play an essential role to develop a better understanding of plant traits, genotypes, stresses, and other related features. It is regarded as essential concept as it facilitates development in several fields such as botany, agronomy, and genetics. Plant phenotype helps in acquiring relevant information about plant organs and whole features that allows the farmers to make informed plant cropping decisions. It includes the use of Deep Learning (DL) which is part of a machine learning technique that makes use of several processing layers to provide reliable outcomes from abstraction. DL-based approaches are highly useful in providing a sufficient amount of data related to plant strapping, stresses, and growth indices. Deep learning approaches are highly efficient in analysing plant phenotype and characterizing the phenotyping aspects by classifying the plant stress datasets into open, labelled, and broad-spectrum.  In this paper, a review work makes an attempt to explore the efficiency of deep learning and filtering approaches in plant phenotyping.  The recent works related to the DL principles have been utilized for digital image–based plant stress phenotyping. Then a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Therefore, it is strongly recommended in the study to use the imaging data process so that there is the attainment of accurate information from training datasets by using high-throughput systems like UAVs and other autonomous systems.