Affiliations: [a] Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tirupur, Tamilnadu, India
| [b] Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India
| [c] Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India
| [d] Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India
| [e] Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Tamilnadu, India
Correspondence:
[*]
Corresponding author. S.K. Kabilesh, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tirupur, Tamilnadu, India. E-mail: [email protected].
Abstract: Monitoring fruit quality, volume, and development on the plantation are critical to ensuring that the fruits are harvested at the optimal time. Fruits are more susceptible to the disease while they are actively growing. It is possible to safeguard and enhance agricultural productivity by early detection of fruit diseases. A huge farm makes it tough to inspect each tree to learn about its fruit personally. There are several applications for image processing with the Internet of Things (IoT) in various fields. To safeguard the fruit trees from illness and weather conditions, it is difficult for the farmers and their workers to regularly examine these large areas. With the advent of Precision Farming, a new way of thinking about agriculture has emerged, incorporating cutting-edge technological innovations. One of the modern farmers’ biggest challenges is detecting fruit diseases in their early stages. If infections aren’t identified in time, farmers might see a drop in income. Hence this paper is about an Artificial Intelligence Based Fruit Disease Identification System (AI-FDIS) with a drone system featuring a high-accuracy camera, substantial computing capability, and connectivity for precision farming. As a result, it is possible to monitor large agricultural areas precisely, identify diseased plants, and decide on the chemical to spray and the precise dosage to use. It is connected to a cloud server that receives images and generates information from these images, including crop production projections. The farm base can interface with the system with a user-friendly Human-Robot Interface (HRI). It is possible to handle a vast area of farmland daily using this method. The agricultural drone is used to reduce environmental impact and boost crop productivity.
Keywords: Fruit quality, Internet of Things (IoT), fruit disease, artificial intelligence, drone system
