Real-time monitoring of water requirement in protected farms by using polynomial neural networks and image processing

Abstract

The monitoring of water requirement in irrigation areas is mostly performed by on-farm methods like utilization of soil probes, tensiometers, or neutron probes. The probes are placed into the soil collected from different depths of the root zone of the crop. But such procedures are found to be time-consuming. As a result, non-portable capacitance-based probes were nowadays utilized for monitoring of soil moisture. However, the sensor-based non-portable system is expensive and out of reach of ordinary farmers. But an absence of on-time monitoring of soil moisture in the root zone of the soil often results in crop failure and incurs a substantial loss on the cultivators. In the present investigation, a real-time inexpensive water monitoring system was proposed to monitor soil moisture in the root zone of a crop such that both time and expenditure can be reduced. The present study is an attempt to develop a real-time monitoring process for crop water requirement (CWR) in protected farm irrigation systems as a function of the significant parameters such as soil porosity (SP), water availability, crop biomass equivalent (CBE), frequency of nutrient application, frequency of irrigation, and CWR. A systematic literature review was performed to identify parameters for CWR, which were then selected by a relevant group of experts on the field. A two-step methodology was followed to develop a function that can automatically estimate water requirement in the root zone of the crop. In the first step, a new probability optimization technique (POT) was proposed for the identification of the priority value of the selected parameters to generate an ideal scenario. In the second step, the index, developed from the parameters and respective priorities selected in the first step, was predicted recurring to polynomial neural network models. The implementation of the nonlinear transfer function in the development of the neural network framework ensures generation of a platform-independent model, which can be embedded to monitor watering requirement for crops cultivated in a protected farm concept. The data of SP and CBE were retrieved from two separate indices (index of soil porosity and biomass index) calculated from images captured from the root and surface areas of the crops. Here, the POT method * Amaresh Sarkar