Recognition　and　counting　of　greenhouse　pests　are　important　for　monitoring　and　forecasting　pest　population　dynamics.　This　study　used　image　processing　techniques　to　recognize　and　count　whiteflies　and　thrips　on　a　sticky　trap　located　in　a　greenhouse　environment.　The　digital　images　of　sticky　traps　were　collected　using　an　image-acquisition　system　under　different　greenhouse　conditions.　If　a　single　color　space　is　used,　it　is　difficult　to　segment　the　small　pests　correctly　because　of　the　detrimental　effects　of　non-uniform　illumination　in　complex　scenarios.　Therefore,　a　method　that　first　segments　object　pests　in　two　color　spaces　using　the　Prewitt　operator　in　I　component　of　the　hue-saturation-intensity　(HSI)　color　space　and　the　Canny　operator　in　the　B　component　of　the　Lab　color　space　was　proposed.　Then,　the　segmented　results　for　the　two-color　spaces　were　summed　and　achieved　91.57%　segmentation　accuracy.　Next,　because　different　features　of　pests　contribute　differently　to　the　classification　of　pest　species,　the　study　extracted　multiple　features　(e.g.,　color　and　shape　features)　in　different　color　spaces　for　each　segmented　pest　region　to　improve　the　recognition　performance.　Twenty　decision　trees　were　used　to　form　a　strong　ensemble　learning　classifier　that　used　a　majority　voting　mechanism　and　obtains　95.73%　recognition　accuracy.　The　proposed　method　is　a　feasible　and　effective　way　to　process　greenhouse　pest　images.　The　system　accurately　recognized　and　counted　pests　in　sticky　trap　images　captured　under　real　greenhouse　conditions.