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Researchers from the University of California developed a robot that traverses citrus orchards in Riverside to identify excess and lack of irrigation tree by tree, reducing water waste, avoiding fertilizer loss in the soil, and paving the way for more precise agriculture in times of drought.
Researchers from the University of California developed a robot to irrigate trees individually in citrus orchards in Riverside, California, reducing water waste and avoiding fertilizer loss in the soil. The system detects excess and lack of irrigation tree by tree, showing that neighboring plants can have very different water needs.
The proposal changes the logic of agricultural irrigation. Instead of applying the same amount of water across the entire area, the method adjusts each application to the actual conditions of each tree, something considered relevant in a scenario of recurring droughts and rising water costs.
In tests, the robot identified moisture patterns that fixed sensors could not capture. The team led by Elia Scudiero correlated these readings with actual soil measurements and found that the water distributed by the sprinklers is not retained uniformly.
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This behavior creates areas where water accumulates and others where it is lost quickly. In practice, this means that uniform irrigation can leave some trees lacking water and others with excess, even when they are side by side.
Robot reveals invisible differences between neighboring trees
The study showed that the terrain within the same orchard does not behave homogeneously. In the same row, one tree may be in clay soil, which retains water longer, while another grows in sandy soil, where water drains quickly.
Additionally, micro-sprinkler systems only wet specific parts of the soil. This detail further amplifies the differences and forms an invisible mosaic of moisture that directly affects the condition of each tree.
The practical conclusion is straightforward: watering everything the same way, in such a heterogeneous environment, is not only inefficient. In some cases, this pattern can also be detrimental to plant development.
The robot was designed precisely to read these variations more accurately. As it traverses the orchard, it generates detailed moisture maps that allow identifying which trees are in water deficit and which receive excess water.
This type of mapping paves the way for more precise irrigation. Each tree can receive exactly what it needs, neither more nor less, with lower resource consumption and greater capacity to withstand water stress.
How the robot measures moisture and reduces dependence on fixed sensors
Instead of relying solely on buried sensors, the robot measures the electrical conductivity of the soil. This indicator works indirectly to indicate moisture: the wetter the soil, the better it conducts electricity, although salinity and temperature also influence the result.
To make the readings more accurate, the researchers combined the robot’s mobile data with point measurements already installed in the field. This strategy allowed for system calibration and generated more comprehensive maps than those obtained with fixed sensors alone.
This hybrid approach reduces the need for large technological deployments in the soil. Since fixed sensors are often expensive to install and maintain, the ability to work with fewer calibration points represents a direct advantage in cost and operation.
The study showed that only four to six calibration points per plot are needed to maintain high accuracy in the model. This means lower initial investment, less maintenance, and a quicker return in the form of water savings.
In a sector where every cost matters, this efficiency can be crucial for the adoption of technology. The robot provides a broader view of the terrain without requiring an extensive structure of buried sensors throughout the area.
Another important point is that the system does not rely on intuition. Decisions are made based on real data collected in the orchard itself, with localized readings and responses adapted to the conditions of each tree.
Excess water affects roots and leads fertilizers beyond the useful zone
The researchers emphasized that excess water is not always perceived as an immediate problem, but it can cause significant damage. When the soil becomes saturated, the roots lose access to oxygen and have difficulty absorbing nutrients.
Constantly wet soils also favor the emergence of root diseases. This type of problem is silent, progressive, and difficult to reverse once established.
The maps generated by the robot help maintain moisture within an ideal range. This way, the tree can grow without suffering from water stress and without facing soil saturation.
Excess irrigation also has a direct impact on fertilizers. When water passes through the zone where roots can absorb nutrients, it carries dissolved compounds, especially nitrogen, to deeper layers of the soil.
This process reduces the nutritional efficiency of the crops. At the same time, it increases the risk of aquifer contamination, a problem that has already caused environmental impacts in areas of intensive agriculture.
By precisely adjusting irrigation, the system reduces this risk of nutrient loss. The savings in this case are not limited to water use but also to a more efficient utilization of fertilizers applied in the field.
Development started in 2019 and the next step is to expand autonomy
The robot did not emerge quickly. Its development began in 2019 and is based on more than a decade of research on soil signal interpretation, which shows a long process of testing, adjustments, and field validation.
During the tests mentioned in the study, the equipment was operated manually. Even so, the project already allows for automation, and in other evaluations, the system demonstrated the ability to traverse entire orchards on a single charge.
The next steps involve integrating autonomous navigation, increasing resistance to field conditions, and adapting the system to different crops. Companies in the AgTech sector are already exploring similar paths, combining robotics, sensors, and data analysis to optimize resource use.
The researchers also pointed out limitations. The mobile sensor measures deeper soil layers than some reference sensors, which can lead to discrepancies in certain cases.
Another point is that the results came from two specific orchards in California. Different soils, climates, and crops can alter the system’s behavior and require new validations.
Still, the advancement is regarded as an important step in the digitalization of agriculture. If large-scale tests confirm the observed performance, the robot could help consolidate localized irrigation, with less water, fewer lost fertilizers, reduced pollution risk, and more efficient resource use in the field.
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