Predicting Potato Crop Yield With Latest Deep Learning Appraoches for Sustainable Agriculture

Accurate prediction of crops' yield is essential to develop farm management and food security. This paper presents the use of a hybrid deep model of Recurrent Neural Networks (RNNs) with ResNet-59 to enhance potato crop yield prediction for sustainable agriculture. Extracted high-resolution spatial features of crop health, soil, and disease from drone and satellite imagery are identified for essential factors to use in predictive modeling. On the other hand, RNN learns long-term dependencies and seasonality influencing potato yield by being trained on time-series data, such as past weather data, soil moisture level, and yield data. Through the integration of temporal and spatial data, the novel approach enhances the precision of forecasts and enables farmers to take optimal decisions. With a strong system of real-time yield estimation, efficient utilization of resources, and climate-resilient agricultural practices, the sequence processing capability of RNN and feature extraction capability of ResNet-59 are combined. With minimum wastage of resources and maximum output, the process is a part of precision agriculture. The study indicates that the hybrid deep learning model can be employed to enhance agricultural sustainability and is therefore a valuable tool for farmers, scientists, and policy makers who would like to produce potatoes sustainably and efficiently.