Modified Barnacles Mating Optimization with Deep Learning Based Weed Detection Model for Smart Agriculture

Abstract

Weed control is a significant means to enhance crop production. Weeds are accountable for 45% of the agriculture sector’s crop losses, which primarily occur because of competition with crops. Accurate and rapid weed detection in agricultural fields was a difficult task because of the presence of a wide range of weed species at various densities and growth phases. Presently, several smart agriculture tasks, such as weed detection, plant disease detection, species identification, water and soil conservation, and crop yield prediction, can be realized by using technology. In this article, we propose a Modified Barnacles Mating Optimization with Deep Learning based weed detection (MBMODL-WD) technique. The MBMODL-WD technique aims to automatically identify the weeds in the agricultural field. Primarily, the presented MBMODL-WD technique uses the Gabor filtering (GF) technique for the noise removal process. For automated weed detection, the presented MBMODL-WD technique uses the DenseNet-121 model as feature extraction with the MBMO algorithm as hyperparameter optimization. The design of the MBMO algorithm involves the integration of self-population-based initialization with the standard BMO algorithm. At last, the Elman Neural Network (ENN) method was applied for the weed classification process. To demonstrate the enhanced performance of the MBMODL-WD approach, a series of simulation analyses were performed. A comprehensive set of simulations highlighted the enhanced performance of the presented MBMODL-WD methodology over other DL models with a maximum accuracy of 98.99%.

Keywords

• smart agriculture
• weed management
• crop productivity
• computer vision
• deep learning