Enriching Crop Yield and Price Prediction Using Transformers
Shubham Gade
Amita Singh
Sanjay Patil
ABSTRACT:
Most developing and underdeveloped countries rely heavily on agriculture and its byproducts for their economic growth. The farmers of these countries hugely rely on the weather to get a good crop yield; due to global warming, there have been a lot of changes in the weather, which leads to untimely rain or no rain at all, extremely windy, cold, and many types of harsh conditions. Because of all this most of the farmers lose the good crop yield leads into a hand-to-mouth situation for farmers and in some worst cases, may lead to the suicide or bankruptcy of the farmers in a country like India. Artificial intelligence plays a crucial role in predicting crop yield and market price before seeding, which can ultimately improve farmer’s living standards by enhancing agricultural practices and making the right decisions. Usually, we use machine learning and deep learning models to predict crop yield and market price individually; hybridizing these models could potentially yield better results by the usage of transformers with deep learning mechanism. Therefore, the designed model utilizes the dataset of crop yield and price, applying temporal fusion transformers to obtain precise predictions. The obtained results of RMSE are scrutinized to check the authenticity of the fusion of neural networks.
Published in: International Journal of Research in Engineering, Science and Management (Volume 7, Issue 12, December 2024)
Page(s): 87-93
Date of Publication: 18/12/2024
Publisher: IJRESM
Cite as: Shubham Gade, Amita Singh, Sanjay Patil, “Enriching Crop Yield and Price Prediction Using Transformers,” in International Journal of Research in Engineering, Science and Management, vol. 7, no. 12, pp. 87-93, December 2024.