. Understanding heat and water stress on field crops is an increasingly important consideration in management decisions. For irrigated crops, detection of crop water stress can be used to regulate irrigation scheduling. In dryland cropping systems, knowing which cultivars are prone to heat stress can help detect drought tolerant cultivars and disambiguate other sources of stress, such as nutrient deficiencies. In either case, surface temperature measurements can act as an indicator of crop water stress, but methods like infrared thermometry give a mix of crop and soil temperatures that may be sunlit or shaded. We present a design for an inexpensive color/thermal imager which extracts surface specific temperatures using image segmentation. The device was tested at two sites in different cropping systems (Pullman, Washington and Bushland, Texas) and measured the temperatures of soil, residue, vegetation, and snow, under both shadowed and sunlit conditions. A multilayer artificial neural network was optimized and trained to segment images, using image features, including three different color spaces, and three different texture features, all applied at three different scales. The trained model had accuracies (correctly labeled pixels over all pixels) of 0.8902 for an initial prototype with lower resolution images, and 0.8797 for a higher-resolution version. Segmented components had surface temperatures that indicate shadow was cooler than sunlit portions, vegetation was cooler than soil, and residue was hotter than soil, all as expected.