Anwesa Choudhuri

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About

Anwesa Choudhuri is a researcher at the forefront of agricultural robotics and neuromorphic computing, whose work bridges the gap between field biology and intelligent machine perception. Her primary research focuses on developing robust computer vision and deep learning solutions for autonomous systems operating in complex, unstructured environments. Choudhuri’s most impactful contribution is her work on crop stem width estimation, a critical yet traditionally labor-intensive phenotype for plant breeders. In her highly cited 2024 paper, she introduced two novel methods for accurately measuring stem width from small mobile robots navigating cluttered field conditions, directly addressing a key bottleneck in high-throughput plant phenotyping. This work has already garnered 6 citations, signaling its importance to the precision agriculture community. More recently, Choudhuri has pushed the boundaries of energy-efficient AI with her 2025 work on LoCS-Net, a localized convolutional spiking neural network designed for fast visual place recognition. By leveraging the temporal dynamics of spiking neurons, this architecture promises to dramatically reduce power consumption for long-duration robotic navigation. Through her innovative fusion of agronomy and cutting-edge neural architectures, Choudhuri is shaping a future where robots can see, learn, and act with unprecedented efficiency in the wild.