Smart Sensing Systems and AI for Precision Agriculture in Climate Changes Era

Nowadays when the global population is growing by more than 80 million a year reported studies are predicting an increasing pressure on the planet's natural resources including food resources. The situation is going worst when unpredictable meteorologic events are running up in the context of great climate changes related to the global effect of anthropogenic greenhouse emissions. In this context precision agriculture (PA) combines technologies and practices to optimize agricultural production through specific farm management are considered.

At the same PA focuses on the accuracy of operations considering the place, time to act and method to be applied. Agricultural operations are carried out to reach the production goals using the information provided by the smart sensors and instrumentation increasing the sustainability of operations. Distributed smart sensing system characterized by fixed and mobile nodes (associated with Unnamed Aerial Vehicle (UAV)) is used to turn farming operations into data, and to make future operations a data-driven one. These new including edge and cloud computing that are capable to run artificial intelligence algorithms may contribute to a slight replacement of human decisions based on their accumulated experience with a machine-based decision. This new way to act in agriculture in a digital form combining technologies such as smart sensors, cloud, and mobile computing, data science is related to the fact that classical decisions cannot be applied nowadays when the cultivated areas are much extended, and the adverse meteorological events are occurring frequently that conduct to miss-management with yield losses.

Using smart sensors computation and data analysis the applied quantity of water and fertilizers is optimized. Weather stations could provide additional information such as ambient temperature, relative humidity, and wind velocity that are also used together soil measured quantities such as moisture, pH, conductivity, temperature, and macronutrients concentration (Nitrogen, Potassium, Calcium) to create models to be used for farm operation optimization. Data from distributed sensing systems on the crop field can be also used to avoid plant stress phenomena (e.g. plant water stress). Data mining is successfully applied in PA being associated with data analysis of massive data.

In this talk, we’ll see together the meaning of precision agriculture in the context of heavy uncertainty associated with climate change. IoT ecosystem for precision agriculture will be discussed including multimodal sensing and artificial intelligence. Referring to sensing as part of the IoT ecosystem in-situ and remote sensing is considered. The agriculture UAV imagery and satellite imagery solutions as so as the relation between the data coming from the smart sensors distributed in the field and acquired images using multispectral imagery techniques will be part of the presentation. Metrological characteristics of smart sensors as so as the calibration procedure for in-situ and remote measurement smart sensing systems will be part of the talk.

Another important technology associated with innovative precision agriculture is related to the development of AI data-driven models for farming operations considering data coming from different sources Examples of data-driven models for smart irrigation and nutrient delivery will be considered.

Challenges to precision agriculture adoption by regular farmers and how the agricultural operation can support the important transformation to become more environmentally sustainable for increased crop quality will be discussed. A specific part of the talk will be climate change, and how this reality will affect the adoption of smart sensing and AI technologies for PA.