SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

Abstract

In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves 88.72% accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia.

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

“…In this scheme, the decomposition of the empirical mode is used to break the climatic data and apply GRU. Sigalingging et al [41] have used a unique deep-learning scheme to improve predictive performance in PA. This scheme selects an adaptive network using contextual information to improve the features.…”