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Automated phenotyping, the automated measurement of plant characteristics, supports breeders and growers in producing vigorous, resilient crops. An early-detection tool can accelerate the discovery of plant resistance and swiftly identify problematic breeding outcomes. Numerous phenotyping objectives of this kind can be met by analyzing images captured by simple, inexpensive RGB-D cameras. The present work focuses on a case study — assessing the degree of resistance that tomato hybrids exhibit against the tomato yellow leaf curl virus (TYLCV) in commercial greenhouses. This poses a tough challenge, given that discriminating between resistance classes from imagery is difficult even for seasoned breeders. A substantial image collection was assembled from a trial encompassing many tomato hybrids with different resistance ratings. Depth data served to pinpoint the highest section of the tomato plant. Subsequently, deep learning networks were deployed to categorize the distinct resistance classes. For spotting plants with visual indicators, our techniques achieved an accuracy of 0.928, a precision of 0.934, and a recall of 0.95. In the multi-class scenario, an accuracy of 0.76 was achieved in identifying the appropriate level, with an error of 0.278. Our pipelines are not exclusively tailored for this task and can be expanded to other undertakings that detect various plant diseases with outward signs, including ToBRFV, mildew, ToMV, and others.