2nd International Conference on Materials Science and Sustainable Manufacturing Technology
A New Approach of Machine Learning and Deep Learning Algorithms Based Crop Yield Prediction
S. Shanthi, K Venkata Sai Vaishnavi, B Supraja, B Vijitha, B Vineela Reddy, Department of CST, Madanapalle Institute of Technology and Science, Andhra Pradesh, India.
The science and skill of nurturing plants and wildlife are referred to as agriculture. India ranks second in the world for farming, which takes up 60.45% of the country's territory. The economy of India is primarily supporting agricultural, agro-industrial sectors. Crop rotation, the consistency of the soil, air and surface temperatures, precipitation, and other elements all have an impact on how well crops are grown. Further crucial are soil constituents including nitrogen, phosphate, and potassium. The corpus of work currently being done in this field includes a crop choice model that makes use of ML methods (Random Forest, Decision Tree, ANN). In this paper, recommended model enhanced using Deep Learning techniques, in addition to crop prediction, precise data on the amounts of necessary soil components and their individual prices are attained. Compared to the present model, it provides a better degree of accuracy. In order to help farmers to predict a profitable crop, analyses the available data. Variables related to the soil and climate taken into consideration to anticipate an acceptable yield. This objective show’s that Python-Based System using cunning strategies for predicting, bountiful harvest possible while using the least amount of resources. In this work, the SVM machine learning algorithm is combined with the LSTM and RNN deep learning algorithms.
Keywords
Random Forest, Decision Tree, ANN, Crop, Yield, Prediction, SVM
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Cite this article
S. Shanthi, K Venkata Sai Vaishnavi, B Supraja, B Vijitha, B Vineela Reddy, “A New Approach of Machine Learning and Deep Learning Algorithms Based Crop Yield Prediction”, Advances in Computational Intelligence in Materials Science, pp. 118-123, May. 2023. doi:10.53759/acims/978-9914-9946-9-8_18
