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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.

Forecasting crop yields provides crucial intelligence for growers of high-value commodities, helping them organize and streamline harvest logistics. The bulk of current yield forecasting approaches rely on manual sampling and statistical procedures, which are intricate, labor-intensive, and time-consuming. Lately, advances in image processing and the broader integration of autonomous systems, such as unmanned aerial vehicles, have enabled swifter, more precise yield forecasting across diverse crops. This work puts forward a novel, rapid, efficient, and accurate image processing algorithm. The segmentation technique proved capable of distinguishing mature citrus trees from their natural surroundings within the CIE (Commission Internationale de l'Éclairage) Lab color space. The influence of the spacing between the camera and the target on resolution and detection accuracy was examined and discussed. Outcomes revealed that distances from 1.524 meters (5 feet) up to 2.134 meters (7 feet) yielded high detection accuracy. The count of correctly recognized fruit attained 91.69%, with an average processing duration of 1.1 s for each image across a set of 132 images. A correlation coefficient (R2) equal to 0.98 was secured between the citrus counting algorithm and counts generated through human visual assessment of 66 sample trees. A comparative review of the introduced technique indicated greater accuracy and robustness than other related research.