Machine Learning and High-Throughput Phenotyping in Precision Agriculture

Dear Colleagues,

Precision agriculture employs diverse technical methods to gather information about the crop growth environment, enabling precise and accurate agricultural micro-management of the entire production process. A pivotal facet of precision agriculture, crop phenotype research delves into the structural attributes of crop individuals or collectives, alongside their functional traits encompassing physical, physiological, and biochemical properties. Consequently, high-throughput phenotypic monitoring can accelerate the entire breeding process and provide important data support for formulating management strategy in precision agriculture.

The evolution of crop phenotype measurement technology encompasses stages such as manual measurement, two-dimensional photogrammetry, and three-dimensional measurement. The ability of remote sensing technology to non-destructively gather surface data through diverse electromagnetic spectrum bands is progressively assuming a more prominent role in precision agriculture. The rapid advancement of spectral and imaging technologies has introduced sophisticated sensors such as multi/hyperspectral, chlorophyll fluorescence, and lidar, offering efficient avenues for procuring crop phenotype data. Deploying a variety of sensors across distinct remote sensing platforms (spaceborne, airborne, and ground-based) facilitates swift acquisition of phenotypic data, enabling comprehensive multi-scale, multi-temporal monitoring of growth dynamics throughout the crop's developmental phase.

Moreover, machine learning has made breakthroughs in the field of remote sensing image processing. In applications such as object recognition and segmentation, image processing based on machine learning performs better than traditional methods. This Special Issue aims to combine machine learning technology and high-throughput phenotypic data to obtain the growth information of crops, indirectly predict the crop yield, monitor crop growth and biotic/abiotic stress responses, and thus realize agricultural precision, digitalization, informatization and intelligent management.

Dr. Jorge Martínez-Guanter
Dr. Akash Ashapure
Prof. Dr. Salah Er-Raki
Guest Editors

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