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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.

The evaluation of plants in greenhouse environments is an essential phase in the development and assessment of new herbicides, as it enables controlled examination of species responses to different formulations and concentrations. To this end, experiments are performed in greenhouses where the injury inflicted on treated plants is monitored daily. Each pot is typically assessed by comparing it against an untreated reference pot, designated as a control. Presently, this task is carried out pot by pot via a time-intensive procedure reliant on visual judgments made by experienced specialists. Digital instruments are needed to shorten the time required and to equip specialists with approaches that are both more objective and more repeatable for quantifying plant injury. A novel solution, grounded in image processing and deep learning, is introduced to estimate plant injury across different developmental stages in the greenhouse. Plants are exposed to multiple injury types and phases, and photographs of them are taken to assemble a dataset. The accessible annotation corresponds to the injury estimation score supplied by the specialists. The proposed framework attempts to replicate the way specialists assess plant injury through a two-stage process. First, the reduction in biomass of the evaluated plant relative to its control plant is quantified; second, potential disease markers on the plant are identified. The former step uses classical image processing methods, while the latter relies on a deep learning-based multi-label classification model for symptom recognition. The algorithm has been validated on two species: Glycine max (soybean) and Amaranthus retroflexus (redroot amaranth). Coefficients of determination (R²) of 0.87 and 0.89 are attained, respectively, for injury estimation. The approach enhances both the efficiency and the objectivity of the current manual workflow.