Phytosanitary treatment is one of the most critical operations in vineyard management. Ideally, the spraying system should treat only the canopy, avoiding drift, leakage and wasting of product where leaves are not present: variable rate distribution can be a successful approach, allowing the minimization of losses and improving economic as well as environmental performances. The target of this paper is to realize a smart control system to spray phytosanitary treatment just on the leaves, optimizing the overall costs/benefits ratio. Four different optical-based systems for leaf recognition are analyzed, and their performances are compared using a synthetic vineyard model. In the paper, we consider the usage of three well-established methods (infrared barriers, LIDAR 2-D and stereoscopic cameras), and we compare them with an innovative low-cost real-time solution based on a suitable computer vision algorithm that uses a simple monocular camera as input. The proposed algorithm, analyzing the sequence of input frames and exploiting the parallax property, estimates the depth map and eventually reconstructs the profile of the vineyard's row to be treated. Finally, the performances obtained by the new method are evaluated and compared with those of the other methods on a well-controlled artificial environment resembling an actual vineyard setup while traveling at standard tractor forward speed.

Structure from Linear Motion (SfLM): An On-the-Go Canopy Profiling System Based on Off-the-Shelf RGB Cameras for Effective Sprayers Control

Marinello F.;Gallina P.
2022

Abstract

Phytosanitary treatment is one of the most critical operations in vineyard management. Ideally, the spraying system should treat only the canopy, avoiding drift, leakage and wasting of product where leaves are not present: variable rate distribution can be a successful approach, allowing the minimization of losses and improving economic as well as environmental performances. The target of this paper is to realize a smart control system to spray phytosanitary treatment just on the leaves, optimizing the overall costs/benefits ratio. Four different optical-based systems for leaf recognition are analyzed, and their performances are compared using a synthetic vineyard model. In the paper, we consider the usage of three well-established methods (infrared barriers, LIDAR 2-D and stereoscopic cameras), and we compare them with an innovative low-cost real-time solution based on a suitable computer vision algorithm that uses a simple monocular camera as input. The proposed algorithm, analyzing the sequence of input frames and exploiting the parallax property, estimates the depth map and eventually reconstructs the profile of the vineyard's row to be treated. Finally, the performances obtained by the new method are evaluated and compared with those of the other methods on a well-controlled artificial environment resembling an actual vineyard setup while traveling at standard tractor forward speed.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3454346
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