15 research outputs found
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
Precision agriculture is considered to be a fundamental approach in pursuing
a low-input, high-efficiency, and sustainable kind of agriculture when
performing site-specific management practices. To achieve this objective, a
reliable and updated description of the local status of crops is required.
Remote sensing, and in particular satellite-based imagery, proved to be a
valuable tool in crop mapping, monitoring, and diseases assessment. However,
freely available satellite imagery with low or moderate resolutions showed some
limits in specific agricultural applications, e.g., where crops are grown by
rows. Indeed, in this framework, the satellite's output could be biased by
intra-row covering, giving inaccurate information about crop status. This paper
presents a novel satellite imagery refinement framework, based on a deep
learning technique which exploits information properly derived from high
resolution images acquired by unmanned aerial vehicle (UAV) airborne
multispectral sensors. To train the convolutional neural network, only a single
UAV-driven dataset is required, making the proposed approach simple and
cost-effective. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as
a case study for validation purposes. Refined satellite-driven normalized
difference vegetation index (NDVI) maps, acquired in four different periods
during the vine growing season, were shown to better describe crop status with
respect to raw datasets by correlation analysis and ANOVA. In addition, using a
K-means based classifier, 3-class vineyard vigor maps were profitably derived
from the NDVI maps, which are a valuable tool for growers
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
Drone and sensor technology for sustainable weed management: a review
Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts
Lora technology and its Iot integration in agriculture: a bibliometric analysis
Introducción: El presente artículo es producto de la revisión “Tecnología LoRa y su integración IoT en la agricultura”, desarrollada en la facultad tecnológica de la Universidad Distrital Francisco José de Caldas realizada durante 2019 y 2020.
Problema: En la actualidad la agricultura enfrenta desafíos y problemas como el calentamiento global, la escasez de agua y la demanda alimentaria. A causa de dichas dificultades se han venido desarrollando tecnología que facilitan el monitoreo de cultivos y granjas. Objetivo: Resaltar las principales y más importantes características en común entre los artículos revisados, identificar las principales revistas que realizan publicaciones durante los años comprendidos entre 2015 y 2020.
Metodología: A partir de esto, se realizó una revisión sistemática en las bases de datos científicas en áreas clave de la agricultura, teniendo en cuenta los artículos publicados entre los años 2015 a 2020. Resultados: Los resultados arrojaron 150 artículos de los cuales sólo 50 cumplieron los criterios de inclusión, Se excluyeron artículos de revisiones, metanálisis y publicaciones en idiomas diferentes al español e inglés. Conclusión: En esta investigación se discute y se analiza los dispositivos más usados dentro de la investigación, módulos LoRa, localizaciones exitosos y beneficios a nivel costos de la implementación de estos sistemas. Originalidad: Mediante la metodología de revisión sistemática y herramientas bibliométricas como bibliometrix R permitieron identificar la información más relevante y los autores más citados. Limitaciones: Existen muy pocos estudios a nivel local que involucran o implementen estas tecnologías.Introduction: This article is the product of the review "LoRa Technology and its IoT integration in agriculture", developed in the technological faculty of the Francisco José de Caldas District University carried out during 2019 and 2020.
Problem: Agriculture: now faces dsnophysis and problems such as global warming, water scarcity and food demand. Because of these difficulties, technology has been developed to facilitate the monitoring of crops and farms.
Objective: Tohighlight the main and most important characteristics in common amongthe revised articles,identify the main journals that publish during the years between 2015 and 2020.
Methodology: From this, a systematic review was carried out in scientific databases in key areas of agriculture,taking into account the articles published between 2015 and 2020.
Results: The results yielded 150 articles of which only 50 met the inclusion criteria, Articles of revisions, meta-analysis and publications in languages other than Spanish and English were excluded.
Conclusion: This researchis discussed and analyzed the most used devices within research, LoRa modules, successful locations and benefits at the cost level of the implementation of these systems.
Originality: Through the systematic review methodology and bibliometric tools such as bibliometrix R they allowed to identify the most relevant information and the most cited authors.
Limitations: Existen very few studies at the local level that involve or implement these technologies
Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered
of great utility by stakeholders (e.g., national and international authorities, farmers, commercial
units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization
(FAO) estimates, around 70% more production of agricultural products will be needed to fulfil
the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs),
especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to
be efficiently integrated into agriculture. The application of RS is indispensable today for a highly
productive and sustainable agriculture. Therefore, the present study draws a general overview of
RS technology with a special focus on the principal platforms of this technology, i.e., satellites and
remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution.
Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery,
in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs),
which is further pushing the boundaries of proficiency through the upgrading of sensors capable of
higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies
(WST) have streamlined real time information acquisition and programming for respective measures.
Improved algorithms and sensors can, not only add significant value to crop data acquisition, but
can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by
making use of cloud computing. The RS technology generates huge sets of data that necessitate the
incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting
the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have
made the orientation of current research towards the estimation of plant physiological traits rather
than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge
technologies are discussed in this study. This study can be helpful for researchers, academics, and
young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702
872181Junta de Andalucia P18-H0-470
Cooperation of unmanned systems for agricultural applications: A theoretical framework
Agriculture 4.0 comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management with the objective of optimising production by accounting for variabilities and uncertainties within agricultural systems. Autonomous ground and aerial vehicles can lead to favourable improvements in management by performing in-field tasks in a time-effective way. In particular, greater benefits can be achieved by allowing cooperation and collaborative action among unmanned vehicles, both aerial and ground, to perform in-field operations in precise and time-effective ways. In this work, the preliminary and crucial step of analysing and understanding the technical and methodological challenges concerning the main problems involved is performed. An overview of the agricultural scenarios that can benefit from using collaborative machines and the corresponding cooperative schemes typically adopted in this framework are presented. A collection of kinematic and dynamic models for different categories of autonomous aerial and ground vehicles is provided, which represents a crucial step in understanding the vehicles behaviour when full autonomy is desired. Last, a collection of the state-of-the-art technologies for the autonomous guidance of drones is provided, summarising their peculiar characteristics, and highlighting their advantages and shortcomings with a specific focus on the Agriculture 4.0 framework. A companion paper reports the application of some of these techniques in a complete case study in sloped vineyards, applying the proposed multi-phase collaborative scheme introduced here