Development of a Matlab Code for the Evaluation of Spray Distribution with Water-Sensitive Paper

Ghiani, Luca; Sassu, Alberto; Piccirilli, Davide; Marcialis, Gian Luca; Gambella, Filippo

doi:10.1007/978-3-030-39299-4_91

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Different types of sensors are applied to develop high-efficiency approaches to optimize input use, maximize crop production, reduce wastes, guarantee environmental sustainability, and obtain economic benefits [ 1 , 2 , 3 , 4 ]. These specific approaches apply to viticulture in terms of efficient use of inputs, such as fertilizers, water, chemicals, or organic products [ 5 , 6 ]. In this context, improving crop protection, the use of machinery and labor for harvesting, pruning, or other crop management operations focuses on improving the efficiency of each plot within the vineyard [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment

Ghiani

Sassu

Palumbo

et al. 2021

Sensors

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An early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on manual counting of fruits or flowers by workers is a time consuming and expensive process and it is not feasible for large fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. In a typical image classification process, the task is not only to specify the presence or absence of a given object on a specific location, while counting how many objects are present in the scene. The success of these tasks largely depends on the availability of a large amount of training samples. This paper presents a detector of bunches of one fruit, grape, based on a deep convolutional neural network trained to detect vine bunches directly on the field. Experimental results show a 91% mean Average Precision.

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Section: Introductionmentioning

confidence: 99%

In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment

Ghiani

Sassu

Palumbo

et al. 2021

Sensors

Self Cite

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“…The black dots on the yellow WPS indicate where the water droplets have impacted the paper. Note that the actual aerosol size is smaller than the observed dot due to the spreading factor (Ghiani et al, 2020) explained in section 2. It should also be appreciated that the WSP is placed relatively low, corresponding to the position of the nose and mouth, where the Air-Screen jet velocity is relatively low.…”

Section: Large Droplets Experiments Resultsmentioning

confidence: 88%

“…By calculating the WPS size/pixel ratio, the droplets spread size could be evaluated. The actual droplet size was then determined using a WPS spreading to droplet size factor (Ghiani, Sassu, Piccirilli, Marcialis, & Gambella, 2020). In so doing, the droplet diameter and population (or frequency) distribution could be determined.…”

Section: Large Droplet Setupmentioning

confidence: 99%

Development and Evaluation of a Fluidic Facemask for COVID-19 Transmission Mitigation

Keisar¹,

Garzozi²,

Shoham³

et al. 2022

Preprint

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A novel fluidic facemask, or Air-Screen, is described and demonstrated experimentally for protection against virus-laden aerosol and droplet infections, such as COVID-19. The Air-Screen employs a small, high-aspect-ratio, crossflow fan mounted on the visor of a filter-covered cap to produce a rectangular air jet, or screen, in front of the wearer’s face. A theoretical model, based on a weekly-advected jet in a crossflow, indicated its effectiveness against a wide spectrum of virus-laden aerosols and droplets. Qualitative flow visualization experiments, using a mannequin, clearly illustrated the Air-Screen’s ability to effectively block airborne droplets from the wearer’s face. Quantitative experiments to simulate droplets produced during sneezing or a wet cough, were propelled (via a transmitter) at an average velocity of 50 m/s at 1 m from the mannequin or a target. The Air-Screen blocked 62% of all droplets with a diameter of less than 150 µm. With an Air-Screen active on the transmitter, 99% of all droplets were blocked and when both mannequin and transmitter Air-Screens were active, 99.8% all droplets were blocked. In contrast to conventional facemasks, the Air-Screen is remarkably effective in protecting the wearer and others from an infected wearer.

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Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation

Rogers,

De La Iglesia,

Zebin

et al. 2024

Neural Comput & Applic

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Modern agriculture relies heavily on the precise application of chemicals such as fertilisers, herbicides, and pesticides, which directly affect both crop yield and environmental footprint. Therefore, it is crucial to assess the accuracy of precision sprayers regarding the spatial location of spray deposits. However, there is currently no fully automated evaluation method for this. In this study, we collected a novel dataset from a precision spot spraying system to enable us to classify and detect spray deposits on target weeds and non-target crops. We employed multiple deep convolutional backbones for this task; subsequently, we have proposed a robustness testing methodology for evaluation purposes. We experimented with two novel data augmentation techniques: subtraction and thresholding which enhanced the classification accuracy and robustness of the developed models. On average, across nine different tests and four distinct convolutional neural networks, subtraction improves robustness by 50.83%, and thresholding increases by 42.26% from a baseline. Additionally, we have presented the results from a novel weakly supervised object detection task using our dataset, establishing a baseline Intersection over Union score of 42.78%. Our proposed pipeline includes an explainable artificial intelligence stage and provides insights not only into the spatial location of the spray deposits but also into the specific filtering methods within that spatial location utilised for classification.

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Development of a Matlab Code for the Evaluation of Spray Distribution with Water-Sensitive Paper

Cited by 5 publications

References 8 publications

In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment

In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment

Development and Evaluation of a Fluidic Facemask for COVID-19 Transmission Mitigation

Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation

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