Results proved that synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as input to a Multi-Layer Perceptron, achieving 100% accuracy for overall stress detection, and 95.6% and 86.7% for water and nitrogen stress identification, respectively. A Stochastic Gradient Descent model, using only the green band, achieved 97.78% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using RGB bands and canopy cover achieved 86.7% for water stress, while RF with the excess green index reached 75.6% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency.