Machine Learning based Micro and Macro Nutrient Deficiency Detection in Okra Plant Leaves: A Review with Current status, Challenges, and Future perspectives.
Keywords:
Macronutrient, okra, feature extraction, classification modelsAbstract
Effective management relies on the timely identification of nutrient deficiencies in okra plants, as thesesignificantly influence both production and quality. This study examines the application of machine learningmethods for detecting micro and macronutrient deficiencies in okra leaves. This study assesses the currentstatus of machine learning methods through feature extraction from leaf images, classification models,
References
Sudhakar, M., & Priya, R. M. (2023). Computer Vision Based Machine Learning and Deep Learning Approaches for
Identification of Nutrient Deficiency in Crops: A Survey. Nature Environment & Pollution Technology, 22(3).
Walsh, J. J., Mangina, E., & Negrão, S. (2024). Advancements in imaging sensors and AI for plant stress detection: A
systematic literature review. Plant Phenomics, 6, 0153.
