Cnn-Bilstm With Attention Mechanism for Crop Yield and Fertilizer Recommendation
Keywords:
Agriculture, Bidirectional Long Short-Term Memory Networks, Crop Yield Prediction, Convolutional Neural Networks, Deep Learning.Abstract
Agriculture has been an incredibly significant means of wealth for humans from our ancient periods. Among the most significant economic sectors in India is agriculture. Approximately 70% of Indians are employed in agriculture, either directly or indirectly. Today's conventional agriculture is inefficient due to a number of issues, including global warming and climate change. Crop yield has decreased as a result of climate change's detrimental effects on the crop production cycle. However, the development of other technologies, such as machine learning and artificial intelligence, has shown great promise in other fields, and incorporating these methods into farming is also a wonderful step forward. Statistical models are usually employed to predict agricultural yield, although this is time-consuming and labor-intensive. The surge in popularity of machine learning and deep learning is a major milestone in the discipline However, in today's complex world, it is a gazillion challenges, because of the climate variations, limited resources, and the need to feed a growing global population. To overcome all these immense and enormous issues, we introduce a novel method, where an ensemble model that combines an attention mechanism, bidirectional long short-term memory networks (BILSTM) with convolutional neural networks (CNN). Through the identification of complex patterns in numerical agricultural statistics, our approach seeks to improve forecast accuracy and offer useful information to stakeholders and farmers. The ensemble model outperforms the individual networks in crop yield prediction by combining their capabilities. The performance rate is measured in terms of accuracy, precision, recall and f-measure.crop dataset scores 98% and fertilizer dataset gives the accuracy of 99%
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