Journal of Physics Conference Series · 2021 · 66 citations · 36 references
AgricultureComputer ScienceCrop SciencesAgricultural AutomationCrop ManagementMachine LearningAgricultural TechnologyYield EstimationYield ForecastingYield PredictionConvolutional Neural NetworksDeep Learning For ForecastingDeep Learning-based ForecastingHybrid Network ModelsMachine Learning Yield ForecastingNeural ForecastingRecurrent Neural Network ForecastingRecurrent Neural Networks
Abstract Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion. Artificial intelligence gives a way to monitor the crop and to predict the yield in an automatized outcome. The study has been made on the deep learning and its hybrid techniques such as Artificial neural network, deep neural network and Recurrent neural network. It helped to identify how the technology of artificial intelligence helps to improve the crop yield. The research study clearly gives the idea and need of recurrent neural network and hybrid network in the field of agriculture. It also shows how it outperforms the other networks such as artificial neural network and convolutional neural network. The results were analyzed and the future perspectives were drawn with the obtained outcome.
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Deep learning based multi-temporal crop classification
Liheng Zhong, Lina Hu, Hang Zhou · Remote Sensing of Environment · 2018 · 993 citations
Agriculture, Agronomy, Crop Sciences +11
Crop Yield Prediction Using Deep Neural Networks
Frontiers in Plant Science · 2019 · 795 citations · Full text