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The study's major goal was to find out the challenges (high cost of input, pest attack, marketing problems, high transportation cost etc.,) that farmers face in the integrated farming system. Farmers' constraints in different enterprises of the integrated farming system were recorded through a well-structured and pretested survey schedule. To confirm their validity and determine the extent to which the identified constraints were seen in crop production as well as in cattle production, goat rearing, backyard poultry production and orange cultivation, the severity of the indicated constraints in the real field condition was measured. The fieldwork was carried out in sixteen villages of the Hadoti region out of which Kota, Bundi, Baran, and Jhalawar were selected randomly. A total of 112 farmers were interviewed and data was gathered through group discussions and personal interviews. The data were quantified by ranking the limitations based on the responses of the respondents.

There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.