6 accepted papers
Accurate soil moisture data is crucial to precise irrigation and manual deployment of existing sensors is labor-intensive and expensive, especially in cornfield environments. We present DRILL, an unmanned ground vehicle (UGV) for autonomously deploying and reading low-cost biodegradable soil moistur…
In this paper, we present an in-row and under-canopy autonomous navigation system for cornfields, called the Purdue Agricultural Navigation System or P-AgNav. Our navigation framework is primarily based on range view images from a 3D light detection and ranging (LiDAR) sensor. P-AgNav is designed fo…
In this paper, we present an in-row and under-canopy autonomous navigation system for cornfields, called the Purdue Agricultural Navigation System or P-AgNav. Our navigation framework is primarily based on range view images from a 3D light detection and ranging (LiDAR) sensor. P-AgNav is designed fo
In this paper, we present an in-row and under-canopy Simultaneous Localization and Mapping (SLAM) framework called the Purdue AgSLAM or P-AgSLAM which is designed for robot pose estimation and agricultural monitoring in cornfields. Our SLAM approach is primarily based on a 3D light detection and ran
Automating leaf detection and physical leaf sample collection using Internet of Things (IoT) technologies is a crucial task in precision agriculture. In this paper, we present a deep learning-based approach for detecting and segmenting crop leaves for robotic physical sampling. We discuss a method f…
In this work, we present a novel agricultural robot called the Purdue AgBot or P-AgBot that has been designed for in-row and under canopy crop monitoring and physical sampling. We suggest approaches to autonomous navigation, crop monitoring, and crop sampling that can be applied in crop rows and und