Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture

Abstract

The growth of the global population, together with climate change and water scarcity, has made the shift towards efficient and sustainable agriculture increasingly important. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators offers great opportunities in this direction since these devices can be easily deployed to implement advanced monitoring and irrigation control techniques at a farm scale, saving energy and water and decreasing costs. This paper proposes an economic and periodic predictive controller taking advantage of the irrigation periodicity. The goal of the controller is to find an irrigation technique that optimizes water and energy consumption while ensuring adequate levels of soil moisture for crops, achieving the maximum crop yield. For this purpose, the developed predictive controller makes use of soil moisture data at different depths, and it formulates a constrained optimization problem that considers energy and water costs, crop transpiration, and an accurate dynamical nonlinear model of the water dynamics in the soil, reflecting the reality. This controller strategy is compared with a classical irrigation strategy adopted by a human expert in a specific case study, demonstrating that it is possible to obtain significant reductions in water and energy consumption without compromising crop yields.

Strawberry Cultivation Techniques

“…For example, low-cost IoT-based sensors and actuators were used to save energy and water, reduce costs, manage irrigation systems properly and effectively, reduce the effects of water-borne diseases and pests, and increase efficiency. Considering the above facts, in a study conducted by Cáceres et al [10], proposed a control system with an economic and predictive feature that provides an advantage for irrigation periodicity. This system aimed to provide maximum efficiency, sufficient soil moisture for crops, and optimize appropriate water and energy consumption.…”

NAM and TPB Approach to Consumers’ Decision-Making Framework in the Context of Indoor Smart Farm Restaurants

“…It is also a more environmentally friendly agriculture system than conventional agriculture, which contributes to environmental pollution through pesticides and carbon emissions [ 1 , 2 ]. Numerous professionals in academia suggest that a shift away from conventional agriculture toward smart farming will make the agriculture sector more environmentally friendly and sustainable [ 5 , 35 , 36 ].…”

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

“…Sustainable precision irrigation is a crucial step toward the attainment of food security, while also achieving water-saving measures to compensate for the uncertainty of rainfall and the effect of water scarcity as a result of drought in many parts of the world. Precision irrigation scheduling is directed toward efficient water usage for each plant, where and when it is needed, in the right amounts, to compensate for water loss either through evapotranspiration, erosion or deep percolation, while preventing over-and under-irrigation [5][6][7][8] With proper irrigation management through effective monitoring and optimal control, water can be saved, as well as providing a reduction in other indirect costs incurred from energy use in the form of electricity or fossil fuel for pumping, for optimal cost-effectiveness [9,10].…”