To　achieve　sustainable　development,　an　efficient　and　time-effective　management　of　waste,　including　household,　industrial,　and　food　waste,　is　crucial.　This　paper　introduces　an　intelligent　food　waste　(FW)　identification　and　analysis　system　based　on　convolution　neural　network　(CNN),　significantly　enhanced　by　the　application　of　fine-tuning.　This　approach　involves　modifying　the　CNN　model　while　retaining　some　original　structures　and　pre-trained　parameters,　and　training　it　using　a　new　dataset.　The　implementation　of　this　method　has　led　to　a　remarkable　increase　in　the　accuracy　of　the　CNN　model　from　94.7%　to　97.6%,　a　reduction　in　the　training　time　cost　by　approximately　82.3%,　and　a　decrease　in　the　number　of　parameters　that　need　to　be　trained　by　about　96.3%.　The　proposed　system,　an　Internet　of　Things　(IoT)　system,　comprises　a　sensing　layer,　network　layer,　data　storage　layer,　and　application　layer.　It　autonomously　identifies　the　type　of　FW　using　CNN,　collecting　and　analysing　food　waste　data,　including　date,　weight,　type,　and　reason.　The　lightweight　neural　network　MobileNetV2　was　employed　because　of　its　low　computing　requirements.　To　further　enhance　accuracy　and　reduce　resource　costs,　fine-tuning　technology　was　applied　using　a　new　training　dataset　and　a　new　output　layer　in　the　neural　network.　This　paper　presents　a　comparative　analysis　of　the　original　and　the　improved　CNN　model,　demonstrating　the　significant　improvements　achieved　through　the　application　of　fine-tuning.　These　results　suggest　that　fine-tuning　can　enhance　the　accuracy　and　training　efficiency　of　CNN　by　saving　time　costs　and　parameters,　thereby　contributing　to　the　application　of　the　CNN　and　fine-tuning.　©　2024　Owner/Author.