In　our　research,　we　aimed　to　address　the　shortcomings　of　traditional　fruit　image　classification　models,　which　struggle　with　inconsistent　lighting,　complex　backgrounds,　and　high　computational　demands.　To　overcome　these　challenges,　we　developed　a　novel　multi-label　classification　method　incorporating　advanced　image　preprocessing　techniques,　such　as　Contrast　Limited　Adaptive　Histogram　Equalization　and　the　Gray　World　algorithm,　which　enhance　image　quality　and　color　balance.　Utilizing　lightweight　encoder-decoder　architectures,　specifically　MobileNet,　DenseNet,　and　EfficientNet,　optimized　with　an　Asymmetric　Binary　Cross-Entropy　Loss　function,　we　improved　model　performance　in　handling　diverse　sample　difficulties.　Furthermore,　Multi-Label　Knowledge　Distillation　(MLKD)　was　implemented　to　transfer　knowledge　from　large,　complex　teacher　models　to　smaller,　efficient　student　models,　thereby　reducing　computational　complexity　without　compromising　accuracy.　Experimental　results　on　the　DeepFruit　dataset,　which　includes　21,122　images　of　20　fruit　categories,　demonstrated　that　our　method　achieved　a　peak　mean　Average　Precision　(mAP)　of　90.2%　using　EfficientNet-B3,　with　a　computational　cost　of　7.9　GFLOPs.　Ablation　studies　confirmed　that　the　integration　of　image　preprocessing,　optimized　loss　functions,　and　knowledge　distillation　significantly　enhances　performance　compared　to　the　baseline　models.　This　innovative　method　offers　a　practical　solution　for　real-time　fruit　classification　on　resource-constrained　devices,　thereby　supporting　advancements　in　smart　agriculture　and　the　food　industry.