BUBMO-based Bi-GRU-CNN model for crop classification with improved feature set: A bigdata perspective

PURPOSE: Big Data's extensive capabilities can aid in addressing the unpredictability of food supply caused by a variety of issues including soil degradation, climate change, water pollution, socio-cultural expansion, governmental laws, and market volatility. However, crop monitoring and classification are critical components of agricultural precision farming. This paper intends to propose a crop classification via a hybrid classification model. DESIGN: First, the input image dataset is subjected to the preprocessing stage to enhance the image dataset by removing noise and blurring the edges with the aid of Gaussian filtering. Second, the improved spider local image feature, median binary pattern and haralick texture features are extracted from the preprocessed image dataset by utilizing the map-reduce framework, to handle big data. Third, the hybrid classification model is proposed that involves two classifiers such as Bi-GRU and CNN. FINDINGS: The weights of both classifier Bi-GRU and CNN were tuned optimally by the proposed hybrid optimization BUBMO that combined both BMO and BWO. The greatest MCC obtained by the propose is 91.47%, whilst the traditional model scored the lowest MCC. ORIGINALITY: The accuracy and improved efficacy of the crop categorization are achieved by employing the suggested classification method.