#Contador Seed Counter Manual manual#

Future developments include grain type and colour categorisation. The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit.

#Contador Seed Counter Manual manual#

The reliability of each trait was validated against ground-truth data from manual measurements. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear.

The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. Current automated methods for phenotyping the maize ear do not capture these spatial features. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding.