Details

Autor(en) / Beteiligte

• Priyadharshini, K.
• Prabavathi, R.
• Devi, V. Brindha
• Subha, P.
• Saranya, S.Mohana
• Kiruthika, K.

Titel

An Enhanced Approach for Crop Yield Prediction System Using Linear Support Vector Machine Model

Ist Teil von

• 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), 2022, p.1-5

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE/IET Electronic Library

Beschreibungen/Notizen

• Smart Agriculture is an emerging progressing field which is used for the management of farming to increase the yield of the crops. Since India is a populated country, urge of food production also increases. This situation is one of the reasons that hindering the development of country. At present farmers get more yield for their crop, but the market price for that crop is very less. To conquer these problems, a machine learning technology is used. The prediction will assist the farmers to select whether the specific crop is suitable for certain season and crop price values. Prediction techniques like linear regression, SVM, KNN method and decision tree of machine learning is widely used in the field of agriculture. This paper proposes a novel method that would deliver suitable support vectors for a SVM classification based on auxiliary information. This optimized method is applied to a real time agricultural application situation which utilize accuracy classification in turn aid production management. The proposed SVM method gives an accuracy of 91% than the existing system. This method can be implemented in several government sectors like APMC, kissan call centre etc., by which the government and farmers can get the information of the future crop yield and the market price.