A Novel Decision Support System for Generating Irrigation Ecolabels Based on the Resource Overutilization Ratio DOI Creative Commons
Sergio Vélez, Raquel Martínez‐Peña, João Valente

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

Abstract As a crucial economic activity, agriculture must consider factors that impact business viability, such as environmental conditions and climate. In planning an agricultural field, particularly for woody crops, it’s to recognize plants are reliable indicators of irrigation efficiency. Unlike herbaceous crops typically planted with system remains unchanged the duration life crop. Initially, is designed based on first year, but after several years, it essential reassess design using new data, plant development, evaluate whether original setup was effective. This static approach, if uncorrected, cannot account developing needs crop, mortality, changes in soil water availability, topographical influences, proper management by farmer. Therefore, regular assessment adjustment necessary ensure long-term efficiency sustainability. To this end, paper introduces novel DSS supported two concepts improving these systems: \(\:Irrigation\:Ecolabel\), Resource Overutilization Ratio (\(\:ROR\)). By FAO-56 Penman-Monteith method calculate current through crop coefficient (Kc) leveraging technologies like drones measure key canopy cover, gathers data. Then, compares information existing system, which, often design. A index developed: \(\:ROR\), which evaluates level excess usage assigns label system. case study vineyard northern Spain revealed opportunities resource savings improvements. Implementing labeling could optimize reduce impact. The datasets can be found public repositories, software open-source.

Язык: Английский

Integrated Framework for Multipurpose UAV Path Planning in Hedgerow Systems Considering the Biophysical Environment DOI Creative Commons
Sergio Vélez, Gonzalo Mier, Mar Ariza-Sentís

и другие.

Crop Protection, Год журнала: 2024, Номер unknown, С. 106992 - 106992

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

3
Assessing the Impact of Overhead Agrivoltaic Systems on GNSS Signal Performance for Precision Agriculture DOI Creative Commons
Sergio Vélez, João Valente, Tamara Bretzel

и другие.

Smart Agricultural Technology, Год журнала: 2024, Номер unknown, С. 100664 - 100664

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

3
Algorithms for Plant Monitoring Applications: A Comprehensive Review DOI Creative Commons
Giovanni Paolo Colucci, Paola Battilani, Marco Camardo Leggieri

и другие.

Algorithms, Год журнала: 2025, Номер 18(2), С. 84 - 84

Опубликована: Фев. 5, 2025

Many sciences exploit algorithms in a large variety of applications. In agronomy, amounts agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. this particular field, the number scientific papers has significantly increased recent years, triggered scientists using artificial intelligence, comprising deep learning and machine methods bots, to process crop, plant, leaf images. Moreover, many other examples can be found, with different applied plant diseases phenology. This paper reviews publications which have appeared past three analyzing used classifying agronomic aims crops applied. Starting from broad selection 6060 papers, we subsequently refined search, reducing 358 research articles 30 comprehensive reviews. By summarizing advantages applying analyses, propose guide farming practitioners, agronomists, researchers, policymakers regarding best practices, challenges, visions counteract effects climate change, promoting transition towards more sustainable, productive, cost-effective encouraging introduction smart technologies.

Язык: Английский

Процитировано

0
Random forest algorithm applied to model soil textural classification in a river basin DOI
Arthur Pereira dos Santos,

Alessandro Xavier da Silva,

Liliane Moreira Nery

и другие.

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(3)

Опубликована: Фев. 26, 2025

Язык: Английский

Процитировано

0
Improved Detection and Location of Small Crop Organs by Fusing UAV Orthophoto Maps and Raw Images DOI Creative Commons
Huaiyang Liu, Huibin Li, Haozhou Wang

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(5), С. 906 - 906

Опубликована: Март 4, 2025

Extracting the quantity and geolocation data of small objects at organ level via large-scale aerial drone monitoring is both essential challenging for precision agriculture. The quality reconstructed digital orthophoto maps (DOMs) often suffers from seamline distortion ghost effects, making it difficult to meet requirements organ-level detection. While raw images do not exhibit these issues, they pose challenges in accurately obtaining detected objects. detection was improved this study through fusion with using EasyIDP tool, thereby establishing a mapping relationship data. Small object conducted by Slicing-Aided Hyper Inference (SAHI) framework YOLOv10n on accelerate inferencing speed farmland. As result, comparing directly DOM, accelerated accuracy improved. proposed SAHI-YOLOv10n achieved mean average (mAP) scores 0.825 0.864, respectively. It also processing latency 1.84 milliseconds 640×640 resolution frames application. Subsequently, novel crop canopy dataset (CCOD-Dataset) created interactive annotation SAHI-YOLOv10n, featuring 3986 410,910 annotated boxes. method demonstrated feasibility detecting three in-field farmlands, potentially benefiting future wide-range applications.

Язык: Английский

Процитировано

0
Recent Progress in the Implementation of Sustainable Farming DOI Creative Commons

M. Muthukumar,

Alagar Karthick

Measurement Sensors, Год журнала: 2025, Номер unknown, С. 101877 - 101877

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A Novel Decision Support System for Generating Irrigation Ecolabels Based on the Resource Overutilization Ratio DOI Creative Commons
Sergio Vélez, Raquel Martínez‐Peña, João Valente

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

Abstract As a crucial economic activity, agriculture must consider factors that impact business viability, such as environmental conditions and climate. In planning an agricultural field, particularly for woody crops, it’s to recognize plants are reliable indicators of irrigation efficiency. Unlike herbaceous crops typically planted with system remains unchanged the duration life crop. Initially, is designed based on first year, but after several years, it essential reassess design using new data, plant development, evaluate whether original setup was effective. This static approach, if uncorrected, cannot account developing needs crop, mortality, changes in soil water availability, topographical influences, proper management by farmer. Therefore, regular assessment adjustment necessary ensure long-term efficiency sustainability. To this end, paper introduces novel DSS supported two concepts improving these systems: \(\:Irrigation\:Ecolabel\), Resource Overutilization Ratio (\(\:ROR\)). By FAO-56 Penman-Monteith method calculate current through crop coefficient (Kc) leveraging technologies like drones measure key canopy cover, gathers data. Then, compares information existing system, which, often design. A index developed: \(\:ROR\), which evaluates level excess usage assigns label system. case study vineyard northern Spain revealed opportunities resource savings improvements. Implementing labeling could optimize reduce impact. The datasets can be found public repositories, software open-source.

Язык: Английский

Процитировано

0