An ML-based Multi-sensor Surveillance system for agriculture field

Nowadays, Large farmers started using modern technology to manage their forms. In these modern technologies, CCTV cameras are mainly used. These CCTV cameras are used for the manual surveillance Of the farm field. Some large farmers are using IOT-based systems for the complete management of the farm field. This system is capable of detecting Stray animals and crops’ properties and needs to use soil moisture sensors humidity sensors etc. Such systems are too costly to afford for any of the small and marginal farmers. This work proposes a low-cost intelligent farm field surveillance system to prevent the farm field from stray animals and stealing farming equipment. A deep learning CNN model is proposed and trained in this work to classify objects into animals, suspicious human activities, non-suspicious human activities, and usual motion activities on farms.