Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Harnessing Machine Learning Paradigm for enhanced Crop Yield Prognostication of Pearl Millet

Harnessing Machine Learning Paradigm for enhanced Crop Yield Prognostication of Pearl Millet

Khurshid, Syyada Shumaila

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798343749403
Publisher: Independently Published
Published: Oct 19 2024
Pages: 52
Weight: 0.32
Height: 0.11 Width: 8.50 Depth: 11.00
Language: English
The incorporation of machine learning in agricultural systems is one of the approaches which offers a great future in the intensification of crop yield as well as food security. The traditional ways of yield forecasting, such as censuses and satellite surveillance, typically are either inaccurate or are too vague. This kind of situating can benefit from more precise and perceptive know-how, such as machine learning, to attain much better and more closely knit crop production. This research particularly deals with exploring and applying machine learning algorithms for the sake of predicting millet output which is a necessary food crop for global food security. Assuredly, classifier models, for example, AdaBoost Regressor, XGBoost Regressor, Decision Tree, Support Vector Machine, and Random Forest, would be used to find the most efficient forecasting method. The best predictive model, which was shown to have the highest accuracy, was AdaBoost Regressor and this model satisfactorily forecast millet yield. The true significance of hiring correctly predictions cannot be underestimated, for they will surely have strong implications on the management decisions and resource allocations. Using machine learning tools the farmer gets the timely and pinpoint data on crop yield expectation, which facilitates precision farming and ensures better productivity and risk management. Apart from this, machine learning together with other state-of-the-art technologies facilitate modelling as well as pave the conditions through which the relationship between the farming technology and the overall advancements can be understood better. With learnings the pattern, the farmers are going to make choices like land area, crop selection, and irrigation stages more intelligently, which in the end aide sustainable agricultural development. Besides, among the techniques of advanced data collection manned by machine learning technology, there are some ways to get rid of the common issues such as biases and inaccuracies in traditional methods. The main objective of big data and complex algorithms is to enable diminishing down of crop yield determinants and developing models which in their turn will become much more forecasting models. Through these studies we will be better acquainted with the intricacies of the agricultural functioning while, at the same time, will potentially be able to achieve better productivity of crops therewith makeup a food secure world .

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