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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.

In recent years, the deployment of artificial intelligence (AI) in farming has emerged as a critical priority. Embracing technological innovation in agriculture, when pursued imaginatively, unlocks significant value. A vital procedure involves managing affected leaves throughout crop cultivation. The prompt identification, categorization, and examination of foliar diseases, together with viable remedies, perpetually foster agricultural progress. A central emphasis of our proposed study is the detection and categorization of diseases across different crops, specifically targeting tomatoes and grapes. The principal aim is to anticipate the type of disease likely to affect grape and tomato foliage at an early stage. Techniques employing Convolutional Neural Networks (CNNs) are harnessed for Multi-Crop Leaf Disease (MCLD) detection. A deep learning-centric model carried out image feature extraction to differentiate between infected and vigorous leaves. The CNN-dependent Visual Geometry Group (VGG) model is applied to achieve superior performance indicators. A collection of crop leaf imagery serves to both train and evaluate the model. Metrics gauging performance, including accuracy, sensitivity, specificity, precision, recall, and the F1-score, were determined and observed. The overarching research aim with the proposed framework is to secure steady enhancements in outcomes. The engineered model distinguishes leaves impacted by disease with heightened precision. In the experimental setup, the suggested investigation achieved 98.40% accuracy for grapevines and 95.71% for tomato plants. This body of work directly contributes to boosting agricultural food output.