International Journal of Science, Engineering and TechnologyAuthors: Abhishek Sharma, V K Jain, Vivek Kumar
Abstract: Agriculture all over the globe is becoming more challenging due to increasing populations, erratic climate conditions, and a scarcity of cultivable land. The ability to predict crop yields accurately is crucial for the development of management strategies for agriculture and for the guarantee of food security. Traditional statistical models oftentimes do not reflect the complex, nonlinear interactions among the factors of crop, soil, and environment. A very effective approach for precision agriculture is deep learning (DL), a branch of artificial intelligence that can model high-dimensional and unstructured data. A comprehensive review of the application of DL models in agriculture as a tool for the prediction and enhancement of production is the focus of this paper. The research indicates improved accuracy of forecasts for multiple crops by evaluation of various architectures, such as CNN, LSTM, and hybrid CNN-LSTM models with attention mechanisms. Moreover, the integration of climate factors, soil properties, and remote sensing data gives valuable insights into the application of yield improvement strategies. There is also discussion of future prospects and challenges, such as transparency, data limitation, and scalable implementation.
DOI: http://doi.org/10.5281/zenodo.21700618
