AgriDeep-net: An advanced deep feature fusion-based technique for enhanced fine-grain image analytics in precision agriculture

Abstract

With the vast diversity and rapidly evolving nature of agricultural landscapes, the need for cutting-edge technological solutions has become increasingly apparent. Addressing the complex challenges of fine-grained agricultural image classification, AgriDeep-Net is introduced as an innovative multi-model deep-learning framework, strategically incorporating advanced techniques to navigate complexities in the field. This precision-driven methodology distinguishes AgriDeep-Net, offering a strategic approach to extract salient and discriminative features from diverse deep-learning models involving highly similar agricultural images marked by low inter-class visual discrimination. Each model is characterized by unique architectural configurations, enabling strategic feature fusion that empowers AgriDeep-Net to capture nuanced semantic information within multi-class agricultural images. The framework adeptly manages the hurdles posed by uneven data distribution, intra-class diversity, and the demands of multi-class classification. Rigorous experimentation underscores AgriDeep-Net's exceptional performance, achieving a testing accuracy of 93.29 % for the ACHENY dataset and an even more impressive 98.44 % for the Indian Basmati seeds dataset. Benchmarking against state-of-the-art deep neural networks, AgriDeep-Net proves its efficacy across diverse datasets collected under real-world and controlled conditions. This framework stands out as a beacon of efficiency and accuracy, eliminating the need for extensive image pre-processing operations and showcasing its potential to empower farmers with precision tools for optimizing crop yields, resource allocation, and swift responses to emerging agricultural challenges.