Detection of sunn pest‐damaged wheat grains using artificial bee colony optimization‐based artificial intelligence techniques

Determination of Optimal Layer Sequence and Thickness for Broadband Multilayer Absorber Design Using Double-Stage Artificial Bee Colony Algorithm

(2019) Enes Yigit et al.   IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES

A study on visual features of leaves in plant identification using artificial intelligence techniques

(2018) Enes Yigit et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Computer vision-based method for classification of wheat grains using artificial neural network

(2016) Kadir Sabanci et al.   JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE

QTL mapping identifies a major locus for resistance in wheat to Sunn pest (Eurygaster integriceps) feeding at the vegetative growth stage

(2016) L. C. Emebiri et al.   THEORETICAL AND APPLIED GENETICS

Detection of the granary weevil based on X-ray images of damaged wheat kernels

(2013) P. Boniecki et al.   JOURNAL OF STORED PRODUCTS RESEARCH

Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging

(2010) Chandra B. Singh et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis

(2010) Muhammad A. Shahin et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging

(2009) C.B. Singh et al.   JOURNAL OF STORED PRODUCTS RESEARCH