Apple Leaf Disease Recognition Based on Optoelectronic Time-Delay Reservoir Computing

The performance of an optoelectronic reservoir computing (RC) system for recognizing apple leaf diseases is numerically investigated. The disease spot feature extraction and selection methods suitable for time-delay RC are investigated. Two masking methods of sequence extension masking and matrix transformation masking are examined, with appropriate parameters, the optoelectronic RC achieves minimum errors of 0.01 and 0.03 under sequence extension masking and matrix masking methods, respectively. The nonlinear dynamics of the optoelectronic reservoir in the absence of data injection are compared with its recognition performance to establish the relationship between recognition capabilities and the system’s nonlinear dynamics. Additionally, we compared the recognition errors of optoelectronic RC, linear RC, and k-nearest neighbor algorithms. It illustrates that optoelectronic RC, under the nonlinear transformation of the Mach-Zehnder Modulator (MZM) and delayed feedback, can map input information nonlinearly to a high-dimensional state space, enabling effective classification along linear hyperplanes in this high-dimensional space.