Abstract:
Crop yield prediction is vital for global foodstuffs and intensely challenging due to its multitudinous factors such as soil, weather, rainfall, temperature, and their in...Show MoreMetadata
Abstract:
Crop yield prediction is vital for global foodstuffs and intensely challenging due to its multitudinous factors such as soil, weather, rainfall, temperature, and their interactions. The influence of weather changes in India impacted in terms of the agricultural crop outcomes over the last decades. The proposed research focuses on the weather condition and assists the farmers to comprehend the crop yields before growing onto farming. This research illustrates a deep learning framework using artificial neural networks (ANN). The offered ANN model, along with further widespread approaches such as bagging classifier, extra tees classifier, linear discriminant analysis (LDA) classifier, quadratic discriminant analysis (QDA) classifier and stochastic gradient boosting (SGB) classifier are used for India's downloaded crop dataset. These proposed six models achieved quite good accuracies. The numerous substantial contributions of the prediction models are the ability to produce the exact predictions. These were succeeded by training the algorithms and naturally to the verify data. Eventually, we tend to compare our study with the previous study's research paper.
Published in: 2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Date of Conference: 19-21 October 2022
Date Added to IEEE Xplore: 25 November 2022
ISBN Information: