Background: Computer vision with deep-learning is emerging as a major approach for non-invasive and non-destructive plant phenotyping. Spikes are the reproductive organs of wheat plants. Detection of spike helps in identifying heading, and counting of the spikes as well as area of the spikes will be useful for determination of the yield of wheat plant. Hence detection and counting of spikes which is considered as the grain bearing organ, has great importance in the phenomics study of large sets of germplasms.
Results: In the present study, we developed an online platform “Web-SpikeSegNet” based on a deep-learning framework for spike detection and counting from the wheat plant’s visual images. This platform implements the “SpikeSegNet” approach developed by Misra et al.(2020), which has proved as an eﬀective and robust approach for spike detection and counting. Architecture of the Web-SpikeSegNet consists of 2 layers. First Layer, Client Side Interface Layer, deals with deals with end user’s requests and its corresponding responses management while the second layer, Server Side Application Layer consisting of spike detection and counting module. The backbone of the spike detection module comprises of deep encoder-decoder network with hourglass for spike segmentation. Spike counting module implements the “Analyze Particle” function of imageJ to count the number of spikes. For evaluating the performance of Web-SpikeSegNet, we acquired wheat plant’s visual images using LemnaTec imaging platform installed at Nanaji Deshmukh Plant Phenomics Centre, ICAR-Indian Agricultural Research Institute, New Delhi, India and the satisfactory segmentation performances were obtained as Type I error 0.00159, Type II error 0.0586, Accuracy 99.65%, Precision 99.59% and F1 score 99.65%.
Conclusions: In this study, freely available web-based software has been developed based on combined digital image analysis and deep learning techniques. As spike detection and counting in wheat phenotyping are closely related to the yield, Web-SpikeSegNet is a signiﬁcant step forward in the ﬁeld of wheat phenotyping and will be very useful to the researchers and students working in the domain.