Highlights and preliminary results for autonomous crop protection

Pérez-Ruiz, M.; Santos, P. González de; Ribeiro, Ángela; Fernández‐Quintanilla, César; Peruzzi, Andrea; Vieri, Marco; Tomic, Slobodanka; Agüera, J.

doi:10.1016/j.compag.2014.11.010

Cited by 74 publications

(40 citation statements)

References 17 publications

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“…This confirmed the results obtained in previous experiments on cross flaming application on maize and other row-spaced summer herbaceous crops (Peruzzi and Raffaelli 2000;Perez-Ruiz et al 2015).…”

Section: Mechanical and Thermal Toolssupporting

confidence: 91%

“…The methodology was the same as that used to assess the precision of herbicide distribution with the RHEA patch sprayer working in wheat and consisted of the positioning of thermal paper strips (able to make easily evident flame presence) 2 m long on the maize rows at the beginning and at the end of any treated patch. These observations allowed confirmation of the correct functioning of the whole system and particularly the degree of integration among the decision making, the perception and the flaming systems (Perez-Ruiz et al 2015;Peruzzi 2014).…”

Section: Assessment Of the Mechanical/thermal System Missionmentioning

confidence: 81%

“…The electronic control device uses the active ingredient flow rate from the pump speed to determine whether a change in active ingredient flow rate is required. The active ingredient flow rate is verified using a mini-flow meter (Perez-Ruiz et al 2015). This development was designed for three different herbicide densities corresponding to three weed/crop ratios: low volume application (100 l/ha), standard volume application (200 l/ha) and high volume application (400 l/ha).…”

Section: Weed Patch Spraying Systemmentioning

confidence: 99%

“…Note that this implementation requires no action from the HLDMS (except a signal to start/finish the mission); therefore, this agricultural implement is autonomous from the control standpoint (Perez-Ruiz et al 2015).…”

Section: Casementioning

confidence: 99%

See 3 more Smart Citations

Fleets of robots for environmentally-safe pest control in agriculture

Santos

Ribeiro

Fernández‐Quintanilla

et al. 2016

Precision Agric

155

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Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced endeffectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops.

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Section: Mechanical and Thermal Toolssupporting

confidence: 91%

Section: Assessment Of the Mechanical/thermal System Missionmentioning

confidence: 81%

Section: Weed Patch Spraying Systemmentioning

confidence: 99%

Section: Casementioning

confidence: 99%

See 2 more Smart Citations

Fleets of robots for environmentally-safe pest control in agriculture

Santos

Ribeiro

Fernández‐Quintanilla

et al. 2016

Precision Agric

155

View full text Add to dashboard Cite

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“…Previous methods in the literature have been designed with the purpose of remote weed mapping using manually-defined rules and have shown great promise in detecting weeds between-crop-rows (Peña et al, 2013;Pérez-Ruiz et al, 2015), but the identification of weeds within-crop-rows still remains an open challenge. The reason is that the algorithm designed for weed detection 50 must be highly robust because very similar seedling crop and weed plants emerge mixed within the crop row.…”

mentioning

confidence: 99%

Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

Pérez-Ortiz

Peña

Gutiérrez

et al. 2016

Expert Systems with Applications

141

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This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-specific control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for weed mapping in both sunflower and maize crops.

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