A deep learning based approach for monitoring sustainable farming practices at a parcel level

Abstract:

Monitoring the effectiveness of policy interventions that promote sustainable farming practices has always been a costly affair. It requires an extensive ground presence which is not always available or reliable. In this paper we present our work so far in the application of deep learning techniques to automate the identification of individual parcels (farms). Our study area is located in the central state of Madhya Pradesh in India, where the average landholding size is around 0.6 hectares per farmer. We created a methodology that uses CNN models for segmentation and Canny Edge detector for generating contours. Our future work concentrates on improving the quality of the reference data and applying additional post-processing methods. Overall, we demonstrate how deep learning could be used for providing specific agronomic advice to individual farmers across large areas and the monitoring thereof, something which is essential in mitigating the effects of climate change.

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