Crop yield prediction based on geographical location for Indian agriculture

Registration ID: IJNRD_190740

Published ID: IJNRD2304146

DOI: Click Here to Get

Ch.Tejasri , A Vinay Kumar , B.Vinay Kumar , Ayesha siddiquah , Ch.Suresh

Soil properties, algorithms, Geographical location, agriculture, crop recommendation,yield

Predicting crop yields is a crucial issue in agriculture because it enables farmers and decision-makers to plan the planting, harvesting, and distribution of their products. The use of machine learning and artificial intelligence approaches to create precise and trustworthy agricultural production prediction models has gained popularity in recent years. These models make use of a variety of data sources, including satellite imaging, soil characteristics, historical crop yields, and weather patterns. In India, agriculture is a significant source of both income and employment. The most frequent issue Indian farmers have is that they choose the wrong crop and don't utilize the right fertilizer for their soil. As a result, they will see a major decline in productivity. The farmer's facility problem has been solved with precision agriculture. They seek to accurately and precisely anticipate agricultural yields so that farmers may improve their planting techniques, cut down on waste, and boost overall output. Random forests, neural networks, and support vector machines are a few of the well-liked machine learning techniques used for agricultural yield prediction. To understand the correlations between input variables and agricultural yields, these algorithms are trained on vast databases of historical crop yields and other pertinent data. By giving farmers useful information and direction on crop management, crop yield prediction models have the potential to change the agricultural industry. They can aid in cost-cutting, cost optimization, and profit maximization.

Published Paper   E-Certificate

Ch.Tejasri, A Vinay Kumar, B.Vinay Kumar, Ayesha siddiquah, & Ch.Suresh (April-2023). Crop yield prediction based on geographical location for Indian agriculture. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), b403-b406. https://ijnrd.org/papers/IJNRD2304146.pdf

Volume 8 Issue 4, April-2023

Pages : b403-b406

Paper Reg. ID: IJNRD_190740

Published Paper Id: IJNRD2304146

Downloads: 000121988

Research Area: Electronics & Communication Engg. 

Country: Martur, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304146.pdf

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