Academic Journal
Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
| العنوان: | Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture |
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| المؤلفون: | O. Sri Nagesh, Raja Rao Budaraju, Shriram S. Kulkarni, M. Vinay, Samuel-Soma M. Ajibade, Meenu Chopra, Malik Jawarneh, Karthikeyan Kaliyaperumal |
| المصدر: | Discover Sustainability, Vol 5, Iss 1, Pp 1-9 (2024) |
| بيانات النشر: | Springer |
| سنة النشر: | 2024 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Boosting, Crop Yield Prediction, Feature Selection, Gray Level Co-occurrence Matrix, AdaBoost Decision Tree, Accuracy, Environmental sciences, GE1-350 |
| الوصف: | Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2662-9984 |
| Relation: | https://doi.org/10.1007/s43621-024-00254-x; https://doaj.org/toc/2662-9984; https://doaj.org/article/9f75d19ef5bf4eae8bdf45dbc466fc94 |
| DOI: | 10.1007/s43621-024-00254-x |
| الاتاحة: | https://doi.org/10.1007/s43621-024-00254-x https://doaj.org/article/9f75d19ef5bf4eae8bdf45dbc466fc94 |
| رقم الانضمام: | edsbas.825F58FF |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 26629984 |
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| DOI: | 10.1007/s43621-024-00254-x |