Abstract
Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects) are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds⋅m-2. Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages.
Highlights
Wheat is one of the main crops cultivated around the world with sowing density usually ranging from 150 to 400 seed·m−2
Plant population density may significantly impact the competition among plants as well as with weeds and affect the effective utilization of available resources including light, water, and nutrients (Shrestha and Steward, 2003; Olsen et al, 2006)
The objective of this study is to develop a system based on high resolution imagery that measures wheat plant population density at early stages
Summary
Wheat is one of the main crops cultivated around the world with sowing density usually ranging from 150 to 400 seed·m−2. Crop density appears as one of the important variables that drive the potential yield. This explains why this information is often used for the management of cultural practices (Godwin and Miller, 2003). Plant population density is still investigated most of the time by visually counting the plants in the field over samples corresponding either to a quadrat or to a segment. This is achieved at the stage when the majority of plants have just emerged and before the beginning of tillering (Norman, 1995) which happens few days to few weeks after emergence. This method is time and labor intensive and may be prone to human error
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