A new mobile application of agricultural pests recognition using deep learning in cloud computing system

• Main Authors: Mohamed Esmail Karar, Fahad Alsunaydi, Sultan Albusaymi, Sultan Alotaibi
• Format: Article
• Language: English
• Published: Elsevier 2021-10-01
• Series: Alexandria Engineering Journal
• Subjects: Smart agriculture; Crop pest; Cloud computing; Deep learning; Faster R-CNN
• Online Access: http://www.sciencedirect.com/science/article/pii/S1110016821001642

Agricultural pests cause between 20 and 40 percent loss of global crop production every year as reported by the Food and Agriculture Organization (FAO). Therefore, smart agriculture presents the best option for farmers to apply artificial intelligence techniques integrated with modern information and communication technology to eliminate these harmful insect pests. Consequently, the productivity of their crops can be increased. Hence, this article introduces a new mobile application to automatically classify pests using a deep-learning solution for supporting specialists and farmers. The developed application utilizes faster region-based convolutional neural network (Faster R-CNN) to accomplish the recognition task of insect pests based on cloud computing. Furthermore, a database of recommended pesticides is linked with the detected crop pests to guide the farmers. This study has been successfully validated on five groups of pests; called Aphids, Cicadellidae, Flax Budworm, Flea Beetles, and Red Spider. The proposed Faster R-CNN showed highest accurate recognition results of 99.0% for all tested pest images. Moreover, our deep learning method outperforms other previous recognition methods, i.e., Single Shot Multi-Box Detector (SSD) MobileNet and traditional back propagation (BP) neural networks. The main prospect of this study is to realize our developed application for on-line recognition of agricultural pests in both the open field such as large farms and greenhouses for specific crops.