Selective spraying of grapevines for disease control using a modular agricultural robot

Oberti, R.; Marchi, Massimo De; Tirelli, Paolo; Calcante, Aldo; Iriti, Marcello; Tona, E.; Hočevar, Marko; Baur, J.; Pfaff, J.; Schütz, Christoph; Ulbrich, H.

doi:10.1016/j.biosystemseng.2015.12.004

Cited by 159 publications

(57 citation statements)

References 27 publications

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“…This would represent a savings of 109.2 mL or 72% of the conventional spraying method. Similar values were reported by [10,12,17,46], who gave a pesticide reduction of 34.5%, 58%, 64% and 66%, respectively, when compared to a conventional homogeneous spraying. The result of these tests confirmed that an inexpensive precision system could confer significant reductions in the use of herbicides.…”

supporting

confidence: 75%

3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions

et al. 2017

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Autonomous selective spraying could be a way for agriculture to reduce production costs, save resources, protect the environment and help to fulfill specific pesticide regulations. The objective of this paper was to investigate the use of a low-cost sonar sensor for autonomous selective spraying of single plants. For this, a belt driven autonomous robot was used with an attached 3-axes frame with three degrees of freedom. In the tool center point (TCP) of the 3-axes frame, a sonar sensor and a spray valve were attached to create a point cloud representation of the surface, detect plants in the area and perform selective spraying. The autonomous robot was tested on replicates of artificial crop plants. The location of each plant was identified out of the acquired point cloud with the help of Euclidian clustering. The gained plant positions were spatially transformed from the coordinates of the sonar sensor to the valve location to determine the exact irrigation points. The results showed that the robot was able to automatically detect the position of each plant with an accuracy of 2.7 cm and could spray on these selected points. This selective spraying reduced the used liquid by 72%, when comparing it to a conventional spraying method in the same conditions.

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supporting

confidence: 75%

3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions

et al. 2017

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“…Very recently, Oberti et al (2016) reported the first fully automatic tests of selective spraying of diseased areas in specialty crops by using a reconfigurable, multifunction agricultural robot developed in the EU project CROPS (www.crops-robots.eu). In a series of greenhouse experiments conducted on grapevine plots exhibiting different levels of powdery mildew disease symptoms, the CROPS robotic system was demonstrated to autonomously detect the disease foci within the healthy canopy and to selectively spray them by means of a sprayer end effector.…”

Section: Literature Review On Precision Spraying Equipmentmentioning

confidence: 99%

“…Starting from the experimental results by Oberti et al (2016), even if very partial, in the analysis it was hypothesised that the robotic technology can enable pesticide saving of 80%, as compared to the reference scenario L0. Also, in this case, the savings are assumed to be obtained without negatively affecting the protection capability of the selective treatments.…”

Section: Advanced Robotic Level As Perspective Scenariomentioning

confidence: 99%

The profitability of precision spraying on specialty crops: a technical–economic analysis of protection equipment at increasing technological levels

2017

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A technical-economic analysis was conducted on three different technological levels of spraying equipment for specialty crops, based on the results on precision spraying technologies reported in scientific literature. The application scenarios referred to general protection protocols against fungal diseases adopted in vineyards and apple orchards in Central-Southern Europe. The analysis evaluated the total costs of protection treatments (equipment ? pesticide costs), comparing the use of conventional air-blast sprayers (referred to as L0), of on-off switching sprayers (L1), and of canopy-optimised distribution sprayers (L2). Pesticide savings from 10 to 35% were associated with equipment L1 and L2, as compared to L0. Within the assumptions made, on grapevines, the conventional sprayer L0 resulted in the most profitable option for vineyard areas smaller than 10 ha; from 10 ha to approximately 100 ha, L1 was the best option, while above 100 ha, the more advanced equipment L2 resulted in the best choice. On apple orchards, L0 was the best option for areas smaller than 17 ha. Above this value, L1 was more profitable, while L2 never proved advantageous. Finally, in a speculation on possible prospectives of precision spraying on specialty crops, the introduction of an autonomous robotic platform able to selectively target the pesticide on diseased areas was hypothesised. The analysis indicated that the purchase price that would make the robotic platform profitable, thanks to the assumed pesticide and labour savings over conventional sprayers, was unrealistically lower than current industrial cost. This study showed that, in current conditions, profitability cannot be the only driver for possible adoption of intelligent robotic platforms for precision spraying on specialty crops, while on-off and canopy-optimised technologies can be profitable over conventional spraying in specific conditions.

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“…In particular, the use of orchard agricultural robotic technology enables regular and quantitative spraying, as well as orchard irrigation, thereby reducing farmers' work and increasing productivity [6,7]. As a result, farmers can avoid excessive use of pesticides, chemical fertilizers, and other resources, and prevent environmental pollution [8].…”

Section: Introductionmentioning

confidence: 99%

Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

et al. 2018

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Abstract:In the case of autonomous orchard navigation, researchers have developed algorithms that utilize features, such as trunks, canopies, and sky in orchards, but there are still various difficulties in recognizing free space for autonomous navigation in a changing agricultural environment. In this study, we applied the Naive Bayesian classification to detect the boundary between the trunk and the ground and propose an algorithm to determine the center line of free space. The naïve Bayesian classification requires a small number of samples for training and a simple training process. In addition, it was able to effectively classify tree trunk's points and noise points of the orchard, which are problematic in vision-based processing, and noise caused by small branches, soil, weeds, and tree shadows on the ground. The performance of the proposed algorithm was investigated using 229 sample images obtained from an image acquisition system with a Complementary Metal Oxide Semiconductor (CMOS) Image Sensor (CIS) camera. The center line detected by the unaided-eye manual decision and the results extracted by the proposed algorithm were compared and analyzed for several parameters. In all compared parameters, extracted center line was more stable than the manual center line results.

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Selective spraying of grapevines for disease control using a modular agricultural robot

Cited by 159 publications

References 27 publications

3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions

3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions

The profitability of precision spraying on specialty crops: a technical–economic analysis of protection equipment at increasing technological levels

Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

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