A comparative study of deep learning and Internet of Things for precision agriculture

Precision farming is made possible by rapid advances in deep learning (DL) and the internet of things (IoT) for agriculture, allowing farmers to upgrade their agriculture operations to sustainably fulfill the future food supply. This paper presents a comprehensive overview of recent research contributions in DL and IoT for precision agriculture. This paper surveys the diverse research on DL applications in agriculture, such as detecting pests, disease, yield, weeds, and soil, including fundamental DL techniques. Also, the work describes the IoT architecture and analyzes sensor categorization, agriculture sensors, and unmanned arial vehicles (UAVs) used in recent research. Besides that, data acquisition, annotation, and augmentation for agriculture datasets were covered, and a few widely used datasets were listed. This work also discusses some challenges and issues that DL and IoT face. Furthermore, the research proposed a bootstrapping approach of Transfer learning where fine-tuned VGG16 is fused with optimized and improved newly built fully connected layers for pest detection. The performance of the proposed model is evaluated and compared with other models, such as custom VGG16 as a classifier; fine-tuned VGG16 is optimized with other optimizers like SGD, RMSProp, and Adam. The results show that the proposed model for pest detection outperforms all other models with an accuracy of 96.58 % and a loss of 0.15%. The review and the proposed work presented in this paper will significantly direct researchers toward DL and IoT for intelligent farming. •Precision agriculture is made possible by combining recent advances in Deep Learning (DL) and Internet of Things (IoT).•DL applications in precision agriculture were discussed such as detection of pest/disease, soil, yield, and more.•IoT architecture, smart devices like sensors and UAVs for smart agriculture were discussed.•Data set study covers data acquisition, augmentation, annotation, and also identifies benchmark dataset for smart agriculture.•The paper proposed a bootstrap approach, where fine-tuned VGG16 is fused with improved dense layers for plant pest detection.