TY - JOUR
T1 - Instance Segmentation and Lightweight CNN-Based Classification of Corn Kernels from Limited Training Samples
AU - Chen Hao
AU - Liu Fang
AU - Zhao Lin
AU - Wei Zhang
JF - Journal of Intelligent Agriculture and Food Systems
Y1 - 2025
VL - 4
IS - 1
SP - 111
N2 - An efficient pipeline for sorting clusters of touching corn kernels—potentially containing sound grains, defective grains, and foreign matter—is introduced. The method operates in two steps. First, a next-generation instance segmentation network, trained entirely on a corpus of synthetically generated images, separates the input scene into distinct individual objects. Second, a purpose-built, lightweight CNN architecture assigns each segmented object to one of three categories (sound, defective, or foreign matter). The segmentation model is learned using a strategy that eliminates the need for labor‑intensive, subjective manual annotation. For the classification step, the custom network is deliberately constructed with a minimal number of layer blocks, yielding a compact architecture suitable for on-device deployment. Experimental evaluations and comparative benchmarks confirm gains in both recognition accuracy and processing speed. The proposed segmentation and classification framework can be readily transferred to other cereal grains.
UR - https://airpublications.com/d541856596
ER -