Recommendations of crop yield and fertilizers using machine learning algorithm. Issue 5 (4th July 2022)

• Record Type: Journal Article
• Authors: Senapati, Biswa Ranjan; Sanskar, ; Trishna, Aditi; Swain, Rakesh Ranjan
• Is Part Of: Journal of information & optimization sciences. Volume 43:Issue 5(2022)
• Page Start: 1029
• Page End: 1037
• Publication Date: 2022-07-04
• Subjects: 68T30; Crop management -- Logistic regression -- Support vector machine; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2022.2094541
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: Agriculture is an important part for a country's both growth and productivity. If agriculture is affected then there will be a huge loss for the country. The farmers are unaware about the types of crops and soil and due to this the growth of crops and quality of soil is hampered. To overcome this problem there are some methods researchers have found using machine learning methods. One of these methods is SVM and Logistic Regression. In this model, the dataset used comprises 22 different types of crops and the amount of potassium, nitrogen and phosphorus needed is obtained using SVM and Logistic Regression. The performance of the model is evaluated in terms of performance metrics like accuracy, precision, recall, F-1 score.