Tomato　is　one　of　the　most　popular　and　widely　grown　vegetables　in　the　world.　Manual　harvesting　of　tomatoes　is　laborious,　time-consuming　and　inefficient,　thus　making　it　somewhat　impractical　for　large-scale　plantations.　Intelligent　robots　have　been　developed　for　harvesting　tomato.　However,　as　the　tomato　is　very　soft　and　thus　especially　prone　to　bruising,　many　significant　technical　challenges　remain　to　be　solved.　In　China,　the　research　on　the　harvesting　robot　is　still　in　its　infancy,　but　considerable　progress　has　been　made　in　many　aspects,　such　as　the　manipulator,　image　recognition,　and　motion　control.　Tomato　targets　extraction　is　the　basis　for　location　and　picking　of　tomato.　Early　extraction　methods　have　certain　limitations,　which　are　difficult　to　meet　the　demand　of　harvest.　In　this　study,　Niblack　self-adaptive　adjustment　parameter　selection　method　was　put　forward　and　successfully　applied　in　extracting　ripe　tomato　in　greenhouse.　This　segmentation　algorithm　was　based　on　traditional　Niblack　algorithm　using　the　correlation　between　global　and　local　grayscale　change　information　of　tomato　image.　The　original　tomato　image　was　firstly　transformed　to　gray　space,　and　the　gray-level　image　was　obtained　using　the　normalized　color　difference　method,　and　segmented　into　the　foreground　and　the　background.　The　normalized　color　difference　method　could　eliminate　the　light　intensity　information　in　the　red　and　green　components.　Then　a　new　Niblack　threshold　segmentation　algorithm　was　used　to　segment　the　gray　image.　The　adjustment　parameter　was　calculated　through　the　expected　value　of　each　window　and　normalized　standard　deviation.　After　denoising,　the　ripe　tomato　object　could　be　easily　extracted　from　segmented　image　by　using　the　minimum　critical　rectangle　method.　In　order　to　compare　different　segmentation　algorithms,　traditional　Niblack　algorithm,　Otsu　algorithm　and　Niblack　self-adaptive　adjustment　parameter　selection　algorithm　had　been　selected　to　perform　the　comparative　analysis.　Experiments　showed　that　the　Otsu　algorithm　could　extract　the　target　of　interest　in　the　image,　which　contributed　significantly　to　the　subsequent　target　recognition　and　the　reduction　in　computation　time.　However,　this　method　may　fail　to　segment　overlapping　tomatoes　into　individual　ones.　For　Otsu　algorithm,　the　threshold　selection　in　each　region　lacked　the　image　characteristics,　which　caused　the　binary　result　to　contain　a　lot　of　background　noise.　Traditional　Niblack　algorithm　exaggerated　image　details　and　got　a　lot　of　unnecessary　edge　information,　which　made　it　difficult　to　separate　the　target　from　background.　Niblack　self-adaptive　adjustment　parameter　selection　algorithm　could　effectively　overcome　the　problem　of　pseudo　noise.　This　approach　has　gotten　a　good　applying　result　in　the　extraction　of　ripe　tomato　object　from　original　images　in　greenhouse　environment.　The　accuracy　rate　of　ripe　tomato　recognition　could　reach　98.3%.　Compared　with　Otsu　algorithm　based　on　normalized　difference　of　red　and　green,　and　traditional　Niblack　segmentation　algorithm,　segmentation　algorithm　based　on　Niblack　self-adaptive　adjustment　parameter　selection　is　more　efficient,　and　its　noise　is　smaller　and　the　process　is　faster.　It　can　meet　the　need　of　the　subsequent　identification　of　tomato　image　and　solve　the　problems　of　low　adaptation　and　pseudo　noise　block　with　traditional　methods.　But　because　of　the　complexity　of　the　object-picking　environment,　the　new　algorithm　remains　to　be　further　improved　in　the　practical　application.　©　2017,　Editorial　Department　of　the　Transactions　of　the　Chinese　Society　of　Agricultural　Engineering.　All　right　reserved.