Bayesian optimization with deep learning based pepper leaf disease detection for decision-making in the agricultural sector

Abstract

Agricultural decision-making involves a complex process of choosing strategies and options to enhance resource utilization, overall productivity, and farming practices. Agricultural stakeholders and farmers regularly make decisions at various levels of the farm cycle, ranging from crop selection and planting to harvesting and marketing. In agriculture, where crop health has played a central role in economic and yield outcomes, incorporating deep learning (DL) techniques has developed as a transformative force for the decision-making process. DL techniques, with their capability to discern subtle variations and complex patterns in plant health, empower agricultural experts and farmers to make informed decisions based on data-driven, real-time insights. Thus, we presented a Bayesian optimizer with deep learning based pepper leaf disease detection for decision making (BODL-PLDDM) approach in the agricultural sector. The BODL-PLDDM technique aimed to identify the healthy and bacterial spot pepper leaf disease. Primarily, the BODL-PLDDM technique involved a Wiener filtering (WF) approach for pre-processing. Besides, the complex and intrinsic feature patterns could be extracted using the Inception v3 model. Also, the Bayesian optimization (BO) algorithm was used for the optimal hyperparameter selection process. For disease detection, a crayfish optimization algorithm (COA) with a long short-term memory (LSTM) method was employed to identify the precise presence of pepper leaf diseases. The experimentation validation of the BODL-PLDDM system was verified using the Plant Village dataset. The obtained outcomes underlined the promising detection results of the BODL-PLDDM technique over other existing methods.

Keywords

• bayesian optimization
• deep learning
• pepper leaf disease detection
• crayfish optimization algorithm
• agricultural sector