Towards efficient irrigation management at field scale using new technologies: A systematic literature review

Life on earth is linked to water resources. Recently, alarm bells have been ringing in global organizations to raise awareness of the importance of rational use of water resources, which are becoming an increasingly scarce commodity. The majority of the world's freshwater is used for agricultural irrigation, hence there is a need to adopt an intelligent irrigation strategy that will lead to sustainable agricultural management. To reap the full benefits, irrigation strategy must be accompanied by a good understanding of field characteristics. Several studies have benefited from the improvement of new technologies for irrigation scheduling, but taking only soil water properties as a basis for research, and to our knowledge there is no systematic literature review study to date that aims at irrigation scheduling taking into consideration the characteristics of the crop field for intelligent and efficient agricultural management. This literature review article aims to explore the new Internet of Things and Artificial Intelligence technologies used on the one hand for monitoring and predicting the coefficients that control the crop evapotranspiration process responsible for crop water losses, namely the reference crop evapotranspiration coefficient ETo and the crop coefficient Kc, and on the other hand for a good and intelligent understanding of the: physical, chemical, biological and hydrological characteristics of a specific field, and which affect the crop evapotranspiration process and therefore yield. Following a systematic literature review methodology led us to a refined selection of 55 journal articles for further analysis. We have identified that the profitability of a crop field is closely linked to the right strategies adopted in a specific crop plot, and these strategies can only be defined after a good understanding of the field's characteristics. We were able to discuss these field characteristics through the primary studies which enabled us to develop an intelligent model that brings together the different approaches adopted for irrigation scheduling and farm management and to identify gaps and limitations in the use of new technologies for farm management at field scale, and thus pave the way for further research.