I m searching the topic for phd research in machine learning.
is there any scope of plants phenotyping in machine learning
It is an excellent challenge- a new horizon with many possibilities in research and industry. One has to have good experimental data to train and make predictions. The best would be data that is both phenotypic and plant response at gene and biochemical level, and use that into integrating into networks that can be predictive of performance
Yes, Machine learning is good topic in plant phenotyping. With the development of new throughput technologies and generation of massive amounts of phenotypic data especially using precision agriculture, machine learning will be widely used in plant phenotyping. You can use several years of field data to make future predictions using machine learning (eg. neural networks) for yield, disease response, pest management, harvest time etc.
We are using machine learning for predicting water content and water potential from ultrasonic resonance...with excellent results. I recommend the method without any doubt.
Yes and No.
Yes because people already working on Machine learning on monogenic traits. You will have lot of data and reference for the study.
No because you still need to fix major traits which are environmentally regulated like QTLs and you will end up with the increased complexity in the models.
But worth trying.
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