AI/ML Drone-based Arecanut Monitoring System (ADAMS)

Abstract

The Indian economy relies heavily on agriculture with 72% of farmers in the states of Karnataka and Kerala producing 330 million kilograms of arecanuts. However, a rapid increase in young adults leaving the agriculture field brings hardships for older farmers like my grandparents since physical labor is increasingly expensive, dangerous, and scarce. The purpose of this research is to automate arecanut crop monitoring by providing a safe, affordable, user-friendly, and cutting-edge system that utilizes six major elements to achieve the goal: arecanut farm, drone, dataset collection, data annotations, AI/ML Mask R-CNN prediction model, and app. Research about various drones were conducted to ensure the drone abides by Indian UAV regulations. Since there were no pre-existing arecanut datasets, a DJI Mini 2 drone and Canon DSLR Camera were utilized to capture over 2000 arecanut images in India. Creating my own dataset from scratch was challenging, however it was a significant contribution in advancing computer vision. This dataset was stored on Amazon S3 and classified into ripe, unripe, and dry arecanuts. Each image was manually annotated using VGG Image Annotator and passed into a Convolutional Neural Network called Mask R-CNN to create a prediction model. This was trained on Amazon Web Services (AWS) for 100 epochs. Overall, epoch 85 gave accurate predictions. Additionally, instead of the DJI Mini 2 drone, the Skydio drone would be the best option for real world implementations. My research is a practical and innovative solution to alleviate farmers’ financial and physical hardships.