Research and Analysis of the Application of Machine Learning in Agricultural Development

Yimin Yuan

doi:10.62051/pawzg339

Authors

Yimin Yuan

DOI:

https://doi.org/10.62051/pawzg339

Keywords:

Machine learning; Agricultural development; application.

Abstract

Agriculture is the most basic, fundamental and important industry. Now, amid global climate change and resource shortages, agriculture must deal with the challenges of growing demand as the world's population increases This article organizes three aspects of agriculture that need improvement: anticipatory preparation before production, improvement of production methods, and detection and classification of agricultural products, and analyzes how machine learning can help agricultural progress in these three aspects. Residual deep convolution and spatial pyramid pooling algorithms in machine learning can be used to help detect plant pests and diseases. The RF algorithm, XGBoost algorithm, LightGBM algorithm and CatBoos in machine learning can generate landslide susceptibility maps. Deep learning, convolutional neural networks, and support vector machines can identify hybrid wheat. Through this research, it can be determined that machine learning can be of great help to agricultural development, and this help and development is mutual. The significance of this study lies in how machine learning can help agricultural development and face these problems.

Downloads

Download data is not yet available.

References

Awais, M., Naqvi, S.M.Z.A., Zhang, H. et al. AI and machine learning for soil analysis: an assessment of sustainable agricultural practices. Bioresour. Bioprocess. 10, 90 (2023). https: //doi.org/10.1186/s4064 3- 023 - 00710 - y.

Usta, Z., Akıncı, H. & Akın, A.T. Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey. Earth Sci Inform 17, 1459 – 1481 (2024). https://doi.org/10.1007/s12145-024-01259-w.

Wang, B., Zhang, C., Li, Y. et al. An ultra-lightweight efficient network for image-based plant disease and pest infection detection. Precision Agric 24, 1836 – 1861 (2023). https: //doi.org/10.1007/s11119 - 023 - 10020 - 0.

Mokhtar, A., Al-Ansari, N., El-Ssawy, W. et al. Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region. Water Resour Manage 37, 1557 – 1580 (2023). https: //doi.org/10.1007/s11269 - 023 - 03443 - x.

Bhadra, S., Sagan, V., Skobalski, J. et al. End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images. Precision Agric 25, 834–864 (2024). https://doi.org/10.1007/s11119-023-10096-8.

Michael Jaumann, Ertug Olcay, Timo Oksanen. Condition Monitoring using Convolutional Neural Network in Agricultural Machinery - Use Case: Disc Mower. IFAC-PapersOnLine. Volume 55, Issue 32. 2022. Pages 235 - 240.

Dönmez, E., Kılıçarslan, S. & Diker, A. Classification of hazelnut varieties based on bigtransfer deep learning model. Eur Food Res Technol 250, 1433–1442 (2024). https: //doi.org/10.1007/s00217 - 024 - 04468 - 1.

Sonmez, M.E., Sabanci, K. & Aydin, N. Convolutional neural network-support vector machine-based approach for identification of wheat hybrids. Eur Food Res Technol 250, 1353 – 1362 (2024). https: //doi.org/10.1007/s00217 - 024 - 04473 - 4.

Dönmez, E. Hybrid convolutional neural network and multilayer perceptron vision transformer model for wheat species classification task: E-ResMLP+. Eur Food Res Technol 250, 1379 – 1388 (2024). https: //doi.org/10.1007/s00217 - 024 – 04469 - 0.

Ganesan Anandhi, M. Iyapparaja. Systematic approaches to machine learning models for predicting pesticide toxicity. Heliyon. Volume 10, Issue 7. 2024. e28752.

Oktoviany, P., Knobloch, R. & Korn, R. A machine learning-based price state prediction model for agricultural commodities using external factors. Decisions Econ Finan 44, 1063 – 1085 (2021). https: //doi.org/10.1007/s10203 - 021 - 00354 - 7.

Sari, M., Duran, S., Kutlu, H. et al. Various optimized machine learning techniques to predict agricultural commodity prices. Neural Comput & Applic (2024). https: //doi.org/10.1007/s00521-024-09679 - x.