EdgeSoil 2.0-Soil Analyzer Using Convolutional Neural Network and Camera Imaging for Agricultural Robotics

Abstract

Soil is the most important building element of agriculture and its analysis is crucial for healthy plants and a high crop yield. But apart from its importance, soil analysis is a tedious and time-consuming task. This paper presents EdgeSoil 2.0, a non-invasive, accurate, and real-time robotic system for soil pH prediction, a key parameter of soil status for farmers. The EdgeSoil 2.0 predicts the pH value of the soil in real-time, using a live video stream from a webcam with an average of 7 FPS. The method is suitable to be implemented on edge devices necessary for the application: we are using a mobile robot with the NVIDIA Jetson Nano module which is running a pH-estimator trained with a Convolutional Neural Network (CNN) on a novel dataset we built for this purpose. Predictions are performed while the robot is moving over the plowed field before the planting process starts. In order to achieve the best performance, we train the pH-estimator with different input modalities and validate each result using Mean Squared Error (MSE) and Standard Deviation (SD). We are able to achieve accurate results with the MSE value of 0.08, the SD value of 0.15, and with testing results from the field showing up to ± 0.3 deviation from the GT value during prediction, which is sufficient to comply with agricultural standards.