IoFT-FIS: Internet of farm things based prediction for crop pest infestation using optimized fuzzy inference system

Abstract

Advanced farming techniques help to know the appropriate environmental conditions, soil quality, water and fertilizer needs, and crop monitoring during each plant’s growth phase, resulting in higher yields. Also, IoT-compatible crop monitoring and data collection systems can help identify crop diseases and the breeding status of pathogenic pests. Extracting data from samples collected from the complete crop growth cycle acknowledged a plausible relationship between weather parameters, crop yield, and insect reproduction. Data analysis done on the data collected through an IoT monitoring system helped determine the environmental factors supporting the higher pest breeding conditions. The proposed fuzzy inference system’s knowledge base is designed using these weather parameters. The multi-objective evolutionary algorithm uses fuzzy rules to find suitable cropping window and low pest breeding conditions. This proposal identifies crop-sowing windows based on fuzzy logic with maximum crop yield and minimum pest growth by deploying IEEE 802.15.4 wireless IoT-enabled sensor network monitoring infrastructure in medium grass vegetation. Experiments are being done on rice and Sugarcane crops. The experiments were conducted in the agriculture field of Gwalior, Madhya Pradesh, India. The soil moisture, rainfall, temperature, etc., data were collected using the wireless sensor network deployed in the field. Fuzzy logic-based identification of appropriate planting seasons by IoT application development services helps farmers prevent the development of pests and proactively take precautions to achieve maximum yields.