Any agricultural operation (such as cultivation, rotavation, ploughing, and harrowing) includes both productive and non-productive activities (like transportation, stops, and idling) in the field. Non-productive work can mislead the actual load profile, fuel consumption, and emissions. In this project, a machine learning-based methodology has been developed to differentiate between effective operations and non-productive activities, utilizing data collected in the field from data loggers installed on the machinery. Measurements were conducted on various machines across the country in all major applications to minimize the influence of any individual sample deviation and to account for variability in customer operating practices. Few critical parameters such as Engine Speed, Exhaust Gas Temperature, Actual Engine Percentage Torque, GPS Speed etc.) were selected after screening and analyzing more than 100 CAN and GPS parameters. The critical parameters were subsequently integrated with road features and various machine learning algorithms (such as KNN, Decision Tree, and Support Vector Machine (SVM). The results demonstrate that the current methodology effectively differentiates between productive operations and non-productive activities (such as transportation and idling) in major agricultural operations, thereby aiding in design-related decision-making