Autonomous Drone-Based Pollination Systems for Enhancing Crop Yield in Orchards Using IoT and Machine Learning Optimization

¹ Department of CS & IT, Kalinga University, Raipur, India
² Research Scholar, Department of CS & IT, Kalinga University, Raipur, India

^* Corresponding author: ku.roohee.khan@kalingauniversity.ac.in

Abstract

Considered as a groundbreaking advancement in agricultural technology, autonomous drone pollination systems hold great promise for drastically increasing the crop yields in orchards. In this study, the integration of several of the relevant technologies to achieve maximum efficiency of pollination is explored. Drones communicate and control using ESP32 IoT modules and maintain it in a reliable real time fashion. Robust loitoastyxl278 module long range provides connectivity drone operations requires to a wide area. Pollination strategies are enhanced by all PSO algorithms which optimize of the flight path and operational parameters. Wireless Sensor Networks (WSN) monitor the environmental conditions (i.e., temperature and humidity) and the resulting adaptive pollination is a response to real time data. Since accurate navigation and mapping of the orchard is necessary, the use of Simultaneous Localization and Mapping (SLAM) technology ensures that the orchard is covered. It is time to explain their implementation because turning these technologies into reality resulted in very significant improvements in: SNR increased to an average of 25.4 dB, PLR was reduced to 1.6%, Data Throughput was optimized to 450 kbps, and Battery Depletion Rate was lowered to 3.2% per minute. Taken as a whole, these advancements created an increase in crop yield of 15% in the system and proved that the system is proficient in simultaneously improving the orchard efficiency and sustainability. The developed integrated approach is a major step forward in precision agriculture to provide possible solutions to boosting productivity and sustainability.