The most important function for the farmer cultivating more crops is to correctly predict the time and price for crop harvests. However, the existing methods' functions cannot predict the crop harvest time and prices accurately due to the fewer features they use. Therefore, in this existing research work, an innovative method known as the Hyena Zfnet Encoder Framework (HZEF), acting as the forecasting system for accurately estimating crop harvest prices and crop harvest times, has been proposed. The proposed method started by considering crop information, past climatic information, crop harvest, and crop harvest prices. Filtered images were employed for removing image noise obtained from the learned image representation. For selecting features, techniques were applied.Consequently, through the matching of the harvesting data and the climate change data, the harvesting season was projected, and the historical data on the prices was applied in deriving the probable prices. The voice assistant functionality proceeded to analyze the harvesting times and the prices associated with each tested agricultural produce. Finally, the new model attained the R-square value of 99.7%, the Root Mean Square Error value at 0.007%, and the computation time of 0.0003s compared to the conventional models. Keywords: Best harvesting time, Price estimation, Voice assistant, Feature analysis, Deep learning, Climate change
Bhandarkar et al. (Wed,) studied this question.