most citedCrop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning

226 citations · 232 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2020

Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications

Luis G Riera, Matthew E. Carroll, Zhisheng Zhang +8

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop…

cs.CV2020★ 6 cited

Usefulness of interpretability methods to explain deep learning based plant stress phenotyping

Koushik Nagasubramanian, Asheesh K. Singh, Arti Singh +2

Deep learning techniques have been successfully deployed for automating plant stress identification and quantification. In recent years, there is a growing push towards training mo…

cs.CV2020

How useful is Active Learning for Image-based Plant Phenotyping?

Koushik Nagasubramanian, Talukder Z. Jubery, Fateme Fotouhi Ardakani +5

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, succ…

cs.LG2020★ 226 cited

Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning

Johnathon Shook, Tryambak Gangopadhyay, Linjiang Wu +3

Accurate prediction of crop yield supported by scientific and domain-relevant insights, can help improve agricultural breeding, provide monitoring across diverse climatic condition…

q-bio.PE2018

Genetic control and geo-climate adaptation of pod dehiscence provide novel insights into the soybean domestication and expansion

Jiaoping Zhang, Asheesh K. Singh

Loss of pod dehiscence is a key step during soybean [Glycine max (L.) Merr.] domestication. Genome-wide association analysis for soybean shattering identified loci harboring Pdh1,…