Measuring the volume of harvested material behind the machine can be beneficial for various agricultural operations, such as baling, dropping, material decomposition, cultivation, and seeding.
This paper aims to investigate and determine the volume of material for use in various agricultural operations.
This proposed methodology can help to predict the amount of residue available in the field, assess field readiness for the next production cycle, measure residue distribution, determine hay readiness for baling, and evaluate the quantity of hay present in the field, among other applications which would benefit the customer.
Efficient post-harvest residue management is essential for sustainable agriculture. This paper presents an Automated Offboard System that leverages Remote Sensing, IoT, Image Processing, and Machine Learning/Deep Learning (ML/DL) to measure the volume of harvested material in real-time. The system integrates onboard cameras and satellite imagery to analyze the field and top layers of residue, correlating this data with elevation maps to compute harvested material volume. This innovation supports operations such as baling, residue decomposition time and thereby contributing to land preparation. This technique offers benefits like reduced operational costs, labor independence, and enhanced soil nutrient planning.