Advancing Agriculture Predictive Models for Farming Suitability Using Machine Learning
Keywords:
Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Agricultural Forecasting, Soil Suitability Prediction, Remote Sensing Data, Climate ModelingAbstract
To maximise crop yields and assure global food security, modern agriculture increasingly uses predictive modelling to identify whether or not a given parcel of land is appropriate for cultivation. Combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks, this study proposes a novel way for assessing the viability of agriculture. CNNs excel in extracting spatial features from satellite and geographical data, whereas LSTMs are utilised to capture temporal correlations in climate and weather trends. We assembled a variety of data, including satellite photos captured at various times of the day, information about the ground, maps of the region, and historical weather records. The LSTM analyses temporal trends, such as seasonal rainfall and temperature fluctuations, while the CNN extracts key spatial information, such as soil texture and land cover. By integrating these two methods, we may analyse the feasibility of agriculture in both space and time. Initial results demonstrate a significant improvement in forecast accuracy when compared to conventional models, as a result of a more complex understanding of the interplay between geographical and temporal factors impacting agricultural potential. In addition, our CNN+LSTM model gives useful data on locations previously deemed unsuitable for agriculture, so facilitating land rehabilitation and environmentally responsible agriculture. This study's findings have the ability to impact agricultural policy, direct investment toward productive locations, and promote the development of agricultural techniques that can adapt to a variety of climate conditions. This study emphasises the necessity to incorporate cutting-edge machine learning techniques into agricultural prediction models.
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