Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy

Mattivi, Pietro; Pappalardo, Salvatore Eugenio; Nikolić, Nebojša; Mandolesi, Luca; Persichetti, Antonio; M, De Marchi; Masin, Roberta

doi:10.3390/rs13101869

Cited by 36 publications

(35 citation statements)

References 63 publications

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“…Results showed good performances of detecting weed by testing ANN, OBIA, and MLL (Figure 2). Moreover, this study also showed the feasibility of adopting a completely open-source workflow for RGB image processing (OpenDroneMap software) and automatic weed extraction by using open algorithms and packages available in SAGA and QGIS software (Mattivi et al, 2021).…”

Section: Vegetation Monitoringmentioning

confidence: 80%

“…One promising application of automatic mapping specific plant species in organic farming and in agroecology is the use of low-cost commercial drones, equipped with a standard RGB camera. A representative case study in the framework of organic farming is reported by Mattivi et al (2021). In this experimental research, a Parrot Anafi UAV was adopted to automatically extract presence of Sorghum halepense, Chenopodium and Amaranthus retroflexus in a maize-crop field.…”

Section: Vegetation Monitoringmentioning

confidence: 99%

“…Fig.2: Details of the weed map obtained with: (A) expert photo interpretation (reference data), (B) MLC method, (C) ANN method, and (D) OBIA method(Mattivi et al, 2021) …”

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confidence: 99%

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Drones and Geographical Information Technologies in Agroecology and Organic Farming Contributions to Technological Sovereignty

M¹,

Diantini²,

Pappalardo³

2022

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The United Nations decade of ecosystem restoration (2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030), the Glashow Climate Pact (November 2021) reaffirms the role of Nature Based Solutions in the fight against climate change and in building shared adaptation solutions. The Glashow Climate Pact highlights the importance of ensuring the integrity of all ecosystems, the protection of biodiversity "recognized by some cultures as Mother Earth, the importance for some of the concept of 'climate justice', when taking action to address climate change".In April 2020 Boaventura de Souza Santos published the "Cruel Pedagogy of Virus" focusing on how the COVID pandemic/syndemic has arrived at the end of six decades of uneven development and highlights the global predatory capitalism and patriarchy embodied in many development discourses, consolidating social exclusion, resource extraction, human and nature domination, environmental injustice, and accumulation by dispossession.Deconstructing development, sustainable development, sustainable growth asks for recognizing practices of critical development, alternative development, alternatives to development, post-development to embrace what Max-Neef called "the development at human scale".Change starts from new practices, challenging the menu of globalizing universalizing development theories and initiatives to inhabit pluriverses of words and worlds.Agroecology, as young science that is about to turn a century, can contribute in various ways to the current challenges of facing environmental and climate emergency, halting biodiversity loss, pursuing just food systems.The indigenous, peasant, and environmental movements of active citizenship, inspired by agroecology, promote food sovereignty, just food systems, the collaboration between food producers and consumers, the renewed alliance between natural, agricultural and urban ecosystems, technological sovereignty, innovation attentive to human rights.This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology and organic farming. In conventional thinking, the use of new technologies is an almost exclusive domain vi Preface

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Section: Vegetation Monitoringmentioning

confidence: 80%

Section: Vegetation Monitoringmentioning

confidence: 99%

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Drones and Geographical Information Technologies in Agroecology and Organic Farming Contributions to Technological Sovereignty

M¹,

Diantini²,

Pappalardo³

2022

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“…GPS is widely used for weed control in precision agriculture, for example, so mechanical weed control can replace chemical control (Partel et al, 2019). Recently, some studies have used GPS instead of optical sensors (Mattivi et al, 2021). GIS is invaluable in agriculture.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of alternative statistical models for predicting habitat suitability for weeds

et al. 2022

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Sinapis arvensis (wild mustard) is a competitive weed of oilseed rape in Iran. The spatial distribution of S. arvensis and its habitat suitability zonation in Fars Province, Iran, was studied using frequency ratio (FR) and weights-of-evidence (WofE) statistical and probabilistic models in a geographic information system. For this purpose, a dataset was prepared of weed presence/absence in oilseed rape fields across Fars Province and measures of different effective factors, including elevation, distance from roads, distance from rivers, pH, electrical conductivity, mean annual temperature, mean annual rainfall, slope degree, slope aspect, plan curvature and physical properties of the soil were collected from the sites. Boruta machine learning method was used to determine the significance of each variable that was used to map the geographical distribution of wild mustard and the appropriateness of its habitat. Using the area under the curve and receiver operating characteristic, the accuracy of the habitat suitability zonation maps generated from the FR and WofE models was assessed (AUC-ROC). The results indicated that AUC-ROC values for the FR and WofE models were 91% and 88% respectively. FR and WofE are good methods for predicting the likelihood of S. arvensis occurring in a oilseed rape field. Finally, the resulting weed distribution map can aid decision makers and managers in identifying areas that require enhanced management in future plans.

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“…Mattivi et al[54] Wheat Optimized a deep residual convolutional neural network (CNN) (ResNet-18) for classifying weed and crop plants in UAV imagery.…”

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confidence: 99%

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

et al. 2021

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Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

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Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy

Cited by 36 publications

References 63 publications

Drones and Geographical Information Technologies in Agroecology and Organic Farming Contributions to Technological Sovereignty

Drones and Geographical Information Technologies in Agroecology and Organic Farming Contributions to Technological Sovereignty

An evaluation of alternative statistical models for predicting habitat suitability for weeds

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

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