Data Analysis for Centella Asiatica Leaf Disease Prediction in Agriculture Using Machine Learning

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P. Deivendran
C. Shanmuganathan
V.Vinoth Kumar
S. Selvakanmani


Plant diseases reduce agricultural output, which affects the economy. As a result, prediction models for plant disease detection and evaluation must be created.  If caught early enough, the most prevalent disease, fungus infection, can be treated by adopting the proper precautions. A rise in interest in plant studies has been observed recently on a global scale.  Centella asiatica is an important medicinal herb that is widely utilized in the east and is becoming more  well known in the west triterpenoid saponins, and Tamilnadu which make up the majority of centella sciatica’s. Chemical makeup, are regarded to be chiefly responsible for its wide ranging therapeutic effects. Exzema, psoriasis, amenorrhea, illnesses of the female genitourinary system, leprosy, lupus, varicose ulcers, and other skin condition are among the other conditions are among the other conditions for which the herb is recommended. It is also used to alleviate anxiety and improve Due to its extensive positive neuroprotective activity,, centella asiatica has been referred to as a brain tonic. The plant is also examined for its toxicity and potential medication interactions.  Anticonvulsant medications were discovered to interact with asiatica and toxicological research also advised against using them over an extended length of time. Additionally, there are several commercial goods out there that have been utilized mostly for dietary supplements, antioxidants, skin nourishment, and memory enhancement.  More research must be conducted on the cultivation and clinical aspects.

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P. Deivendran, C. Shanmuganathan, V.Vinoth Kumar, & S. Selvakanmani. (2023). Data Analysis for Centella Asiatica Leaf Disease Prediction in Agriculture Using Machine Learning . Journal of Coastal Life Medicine, 11(2), 406–418. Retrieved from


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