Machine Learning based early detection of Yellow leaf disease in arecanut plant using soil samples

Registration ID: IJNRD_192227

Published ID: IJNRD2304477

DOI: Click Here to Get

Shwetha Kamath , Prakhyath Devadiga , Prathiksha G K , Prathiksha S , Shrinidhi Shervegar

Yellow Leaf Disease, Arecanut, Machine Learning, Soil parameters, ID3 Algorithm.

Arecanut is an important cash crop grown extensively in India, especially in the southern states. It is also known as betel nut and is a major source of income for many farmers in the region. Arecanut is consumed widely in the form of chewing and is an integral part of social and religious practices in India. However, one of the major threats to arecanut cultivation is the Yellow Leaf Disease (YLD). The disease is characterized by yellowing of leaves, stunted growth, and reduced yield, leading to significant economic losses for farmers. The disease is also highly contagious and can spread rapidly, making early detection crucial for effective mitigation measures. Traditionally, the detection of YLD has relied on visual symptoms, which can be challenging to identify in the early stages. This delay in detection can result in significant crop losses. Therefore, there is a need for an early detection system that can identify the disease at an early stage and help farmers take necessary measures to control its spread. In this context, machine learning-based solutions using soil samples have emerged as a promising approach for early detection of YLD in arecanut plants. By analyzing various soil parameters, ID3 algorithm can detect the presence of the disease at an early stage. This can help farmers take timely action to prevent the spread of the disease and improve crop yields.

Published Paper   E-Certificate

Shwetha Kamath, Prakhyath Devadiga, Prathiksha G K, Prathiksha S, & Shrinidhi Shervegar (April-2023). Machine Learning based early detection of Yellow leaf disease in arecanut plant using soil samples. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), e546-e551. https://ijnrd.org/papers/IJNRD2304477.pdf

Volume 8 Issue 4, April-2023

Pages : e546-e551

Paper Reg. ID: IJNRD_192227

Published Paper Id: IJNRD2304477

Downloads: 000121984

Research Area: Computer Science & Technology 

Country: Dakshina Kannada, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304477.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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