A method to estimate plant density and plant spacing heterogeneity: application to wheat crops

Abstract

BackgroundPlant density and its non-uniformity drive the competition among plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the plant density in wheat crops from plant counting and reach a given precision.ResultsThree experiments were conducted in 2014 resulting in 14 plots across varied sowing density, cultivars and environmental conditions. The coordinates of the plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate plant density for a given precision. Results suggest that measuring the length of segments containing 90 plants will achieve a precision better than 10%, independently from the plant density. This approach appears more efficient than the usual method based on fixed length segments where the number of plants are counted: the optimal length for a given precision on the density estimation will depend on the actual plant density. The gamma count model parameters may also be used to quantify the heterogeneity of plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive plants should be measured.ConclusionsThis method provides an optimal sampling strategy to estimate the plant density and quantify the plant spacing heterogeneity along the row.

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

“…Seedling stand count is not only critical for determining plant emergence (Jin et al, 2017 ), density (Liu et al, 2017b ) and yield (Zheng et al, 2016 ) in breeding programs, but also important for other related agronomic practices. For example, plant density derived from seedling stand count is considered as one of the first variables commonly measured in agronomical trials (Liu et al, 2017a ). Furthermore, most crops are sown in rows by seeding devices nowadays.…”

“…These results demonstrated the potential of using field-based HTPPs to achieve crop seedling counting. However, these methods for maize stand counting can hardly be applied to other small crops (Liu et al, 2017a ), such as wheat and rapeseed. Maize plants are bigger, with larger plant spacing and more uniform distribution (Jin et al, 2017 ; Liu et al, 2017b ).…”

“…In spite of a number of difficulties, several studies on wheat density estimation have been conducted (Liu et al, 2016 , 2017a , b ; Jin et al, 2017 ). The seedling counting of wheat first started with the extraction of features of segmentation objects.…”

Land-based crop phenotyping by image analysis: consistent canopy characterization from inconsistent field illumination

“…The range of phenotyping that can occur in the field is vast. Hardware solutions that have been proposed range from aerial imaging [ 12 , 13 ], large and expensive purpose-built systems [ 14 , 15 ], small and expensive purpose built systems [ 16 – 18 ] to small and inexpensive systems [ 19 , 20 ]. While each approach possesses distinct advantages and disadvantages, the increase in throughput and volume of high resolution RGB (Red, Green and Blue channel) data offered by imaging of field trials comes at a cost: new methodologies are required to overcome new challenges.…”

A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network

“…Besides the performance of the planter, the location of crops after emergence is influenced by the elimination of early crops due to pests or stressful environmental conditions such as too much or a lack of moisture, low temperature, etc. [ 7 ]. By determining precise locations of crops, in addition to evaluating sowing performance of planters across a field, map-based automatic weed control and special treatment for each individual plant like spraying would be possible.…”