Energy-aware and robust date palm disease classification in the Qassim region using a cooperative hybrid GA–MOPSO on edge devices

In this paper, We formulate date palm leaf disease recognition as a hyperparameter optimization problem with three objectives, jointly maximising Macro–F1, robustness to acquisition shifts, and energy efficiency for compact CNN backbones deployed on edge devices. To address this, we propose a cooperative GA–MOPSO framework in which two solver components (a GA population and a MOPSO population) interact through a shared archive of non-dominated CNN configurations and periodically exchange selected solutions to balance exploration and exploitation. Experiments on a nine-class dataset of 11,420 date palm leaf images from Al–Madinah and Al–Qassim—with testing restricted to Qassim palms to mimic realistic deployment—show that the proposed cooperative GA–MOPSO improves the median hypervolume from 0.70 to 0.76 ( $$\varvec{\Delta }{=}+\varvec{0.06}$$ absolute; about $$\varvec{8.6}\%$$ ), reduces IGD from 0.108 to 0.082 ( $$\varvec{\Delta }{=}-\varvec{0.026}$$ ; about $$\varvec{24.1}\%$$ ) and spacing from 0.042 to 0.031 ( $$\varvec{\Delta }{=}-\varvec{0.011}$$ ; about $$\varvec{26.2}\%$$ ), and enlarges the median archive size from 20 to 25 ( $$\varvec{\Delta }{=}+\varvec{5}$$ ; about $$\varvec{25}\%$$ ) compared with the best baseline, while incurring only a small $$\varvec{\approx } \varvec{6}\%$$ extra runtime over classical MOPSO (4100 s to 4350 s; $$\varvec{\Delta }{=}+\varvec{250}$$ s) and remaining faster than NSGA–II/III.