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You searched for: Journal name Smart agricultural technology Remove constraint Journal name: Smart agricultural technology Publication year rev 7977-2023 Remove constraint Publication year rev: 7977-2023 Source 2023 v.3 Remove constraint Source: 2023 v.3
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1. Acoustic sensors for detecting cow behaviour

Author:
P.R. Shorten
Source:
Smart agricultural technology 2023 v.3 pp. 100071
ISSN:
2772-3755
Subject:
acoustics; cell respiration; cows; differential pressure; environmental impact; feces; microphones; model validation; models; pastures; prediction; technology; vocalization
Abstract:
... Acoustic technologies provide a non-invasive method to generate information about the health, welfare, and environmental impact of livestock. This study demonstrated that rear leg attached acoustic sensors can be used to differentiate between seven different acoustic classes, with six based on cow behaviours (Grazing, Breathing, Walking, Lying Down, Dung, Vocalization, Other) that were obtained fr ...
DOI:
10.1016/j.atech.2022.100071

2. Applications of extended reality (XR) in agriculture, livestock farming, and aquaculture: A review

Author:
Evangelos Anastasiou; Athanasios T. Balafoutis; Spyros Fountas
Source:
Smart agricultural technology 2023 v.3 pp. 100105
ISSN:
2772-3755
Subject:
COVID-19 infection; aquaculture; climate change; energy poverty; freshwater; humans; livestock; population growth; primary sector; technology; urbanization
Abstract:
... The primary sector is facing a series of challenges such as climate change, overpopulation, freshwater scarcity, energy poverty, urbanization and an ageing population. On top of that, the COVID-19 pandemic disrupted the sector even more. Extended Reality (XR) is a promising technology that can enable the solving of the aforementioned challenges. The main aim of this study was to analyze the use of ...
DOI:
10.1016/j.atech.2022.100105

3. Characterization of trichome phenotypes to assess maturation and flower development in Cannabis sativa L. (cannabis) by automatic trichome gland analysis

Author:
D.B. Sutton; Z.K. Punja; G. Hamarneh
Source:
Smart agricultural technology 2023 v.3 pp. 100111
ISSN:
2772-3755
Subject:
Cannabis sativa; algorithms; amber; automation; cannabinoids; color; data collection; death; females; flowering; flowers; fluorescence; geometry; greenhouse production; harvest date; head; industry; phenotype; photography; product quality; terpenoids; trichomes; Canada
Abstract:
... Cannabis (Cannabis sativa L.) is cultivated by licensed producers in Canada for medicinal and recreational uses. The recent legalization of this plant in 2018 has resulted in rapid expansion of the industry, with greenhouse production representing the most common method of cultivation. Female cannabis plants produce inflorescences that contain bracts densely covered by glandular trichomes, which s ...
DOI:
10.1016/j.atech.2022.100111

4. Combined analysis of satellite and ground data for winter wheat yield forecasting

Author:
Camilla Broms; Mikael Nilsson; Andreas Oxenstierna; Alexandros Sopasakis; Karl Åström
Source:
Smart agricultural technology 2023 v.3 pp. 100107
ISSN:
2772-3755
Subject:
clay; decision support systems; game theory; image analysis; magnesium; remote sensing; satellites; soil; variance; weather; winter wheat; Sweden
Abstract:
... We built machine learning and image analysis tools in order to forecast winter wheat yield based on a rich multi dimensional tensor of agricultural information spanning different scales. This information consists of satellite multi-band images, local soil samples obtained from national databases, local weather as well as field data from 23 farms cultivating winter wheat in southern Sweden. This is ...
DOI:
10.1016/j.atech.2022.100107

5. DSSApple: A hybrid expert system for the diagnosis of post-harvest diseases of apple

Author:
Gabriele Sottocornola; Sanja Baric; Maximilian Nocker; Fabio Stella; Markus Zanker
Source:
Smart agricultural technology 2023 v.3 pp. 100070
ISSN:
2772-3755
Subject:
Bayesian theory; Internet; apples; cultivars; decision support systems; diagnostic techniques; fruits; streams
Abstract:
... Post-harvest diseases of apple can cause considerable economic losses. Thus, we developed DSSApple, an interactive web-based decision support system, that helps users to diagnose post-harvest diseases of domesticated apple based on observed macroscopic symptoms on fruit. Specifically, DSSApple is designed as a two-stream hybrid diagnostic tool, that can be effectively used by both expert and non-e ...
DOI:
10.1016/j.atech.2022.100070

6. Data-driven model predictive control for precision irrigation management

Author:
Erion Bwambale; Felix K. Abagale; Geophrey K. Anornu
Source:
Smart agricultural technology 2023 v.3 pp. 100074
ISSN:
2772-3755
Subject:
Internet; climate; data collection; irrigation canals; prediction; soil water; water potential
Abstract:
... The future of agriculture faces a threat from a changing climate and a rapidly growing population. This has put enormous pressure on water and land resources as more food is expected from less inputs. Advancement in smart agriculture through the use of the Internet of Things and improvement in computational power has enabled extensive data collection from agricultural ecosystems. This review intro ...
DOI:
10.1016/j.atech.2022.100074

7. Detecting olive grove abandonment with Sentinel-2 and machine learning: The development of a web-based tool for land management

Author:
Iride Volpi; Susanna Marchi; Ruggero Petacchi; Klean Hoxha; Diego Guidotti
Source:
Smart agricultural technology 2023 v.3 pp. 100068
ISSN:
2772-3755
Subject:
Internet; Olea europaea; abandoned land; agroecosystems; algorithms; data collection; land management; mobile telephones; models; olive oil; olives; profitability; socioeconomics; time series analysis; Italy
Abstract:
... The abandonment of rural areas is an important environmental and socio-economic issue in Europe, threatening the stability and profitability of agricultural production. The identification and quantification of abandoned land is key for temporal and spatial monitoring of the process and for applying alternative management measures. Italy is one of the most important European countries for the produ ...
DOI:
10.1016/j.atech.2022.100068

8. Development and comparison of artificial intelligence models for estimating daily reference evapotranspiration from limited input variables

Author:
Jaydip J. Makwana; Mukesh K. Tiwari; B.S. Deora
Source:
Smart agricultural technology 2023 v.3 pp. 100115
ISSN:
2772-3755
Subject:
artificial intelligence; data collection; equations; evapotranspiration; irrigation water; meteorological data; neural networks; regression analysis; relative humidity; solar radiation; temperature; water allocation; wind speed; India
Abstract:
... The accurate and efficient computational modelling framework for computation of reference evapotranspiration (ET₀) is the need of time and is highly essential for several agricultural, hydrological and hydro-climatological studies and applications. ET₀ has its own role especially for the water resource system management including irrigation water allocation and management, utilization and demand a ...
DOI:
10.1016/j.atech.2022.100115

9. Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning

Author:
Seyed Mohamad Javidan; Ahmad Banakar; Keyvan Asefpour Vakilian; Yiannis Ampatzidis
Source:
Smart agricultural technology 2023 v.3 pp. 100081
ISSN:
2772-3755
Subject:
color; computer vision; crop yield; disease diagnosis; grapes; leaf blight; leaves; measles; principal component analysis; product quality; support vector machines
Abstract:
... Plant diseases often reduce crop yield and product quality; therefore, plant disease diagnosis plays a vital role in farmers’ management decisions. Visual crop inspections by humans are time-consuming and challenging tasks and, practically, can only be performed in small areas at a given time, especially since many diseases have similar symptoms. An intelligent machine vision monitoring system for ...
DOI:
10.1016/j.atech.2022.100081

10. Evaluating biostimulants via high-throughput field phenotyping: Biophysical traits retrieval through PROSAIL inversion

Author:
Giulia Antonucci; Giorgio Impollonia; Michele Croci; Eleonora Potenza; Andrea Marcone; Stefano Amaducci
Source:
Smart agricultural technology 2023 v.3 pp. 100067
ISSN:
2772-3755
Subject:
artificial intelligence; canopy; drought; industry; irrigation; model validation; phenotype; radiative transfer; remote sensing; summer; tomatoes; uncertainty
Abstract:
... Drought management largely depends on the availability of timely, accurate and integrated information about its characteristics. Concurrently, biostimulants could represent a sustainable measure to foster the resilience of cropping systems under water-limited conditions. Nevertheless, scientific recognition of the potential of biostimulants has not grown as fast as the interest from industry: ther ...
DOI:
10.1016/j.atech.2022.100067

11. Field evaluation of a deep learning-based smart variable-rate sprayer for targeted application of agrochemicals

Author:
Aitazaz A. Farooque; Nazar Hussain; Arnold W. Schumann; Farhat Abbas; Hassan Afzaal; Andrew McKenzie-Gopsill; Travis Esau; Qamar Zaman; Xander Wang
Source:
Smart agricultural technology 2023 v.3 pp. 100073
ISSN:
2772-3755
Subject:
Alternaria solani; Chenopodium album; Solanum tuberosum; Spergula arvensis; agrochemicals; blight; corn; environmental stewardship; farms; liquids; plant protection; potatoes; spray volume; sprayers; weeds
Abstract:
... The field performance of a newly developed novel smart variable-rate sprayer was evaluated. The sprayer uses convolutional neural networks (CNNs) for target detection and spot-applications of agrochemicals within potato (Solanum tuberosum L.) fields attacked by lamb's quarters (Chenopodium album L.) and corn spurry (Spergula arvensis L.) weeds and the early blight potato disease caused by Alternar ...
DOI:
10.1016/j.atech.2022.100073

12. HOB-CNN: Hallucination of occluded branches with a convolutional neural network for 2D fruit trees

Author:
Zijue Chen; Keenan Granland; Rhys Newbury; Chao Chen
Source:
Smart agricultural technology 2023 v.3 pp. 100096
ISSN:
2772-3755
Subject:
automation; fruits; labor force; leaves; neural networks; prediction
Abstract:
... Orchard automation has attracted the attention of researchers recently due to the shortage of global labor force. To automate tasks in orchards such as pruning, thinning, and harvesting, a detailed understanding of the tree structure is required. However, occlusions from foliage and fruits can make it challenging to predict the position of occluded trunks and branches. This work proposes a regress ...
DOI:
10.1016/j.atech.2022.100096

13. Indwelling robots for ruminant health monitoring: A review of elements

Author:
Upinder Kaur; Rammohan Sriramdas; Xiaotian Li; Xin Ma; Arunashish Datta; Barbara Roqueto dos Reis; Shreyas Sen; Kristy Daniels; Robin White; Richard M. Voyles; Shashank Priya
Source:
Smart agricultural technology 2023 v.3 pp. 100109
ISSN:
2772-3755
Subject:
biomarkers; chemistry; environmental impact; fermentation; food security; locomotion; nutrition; ruminants
Abstract:
... Maximizing the health, productivity, and sustainability of ruminant animals is an essential societal goal for a variety of reasons including food security, impact on the environment, and global nutrition; however, monitoring the metabolism of ruminant animals is currently a time-consuming and imprecise process. An indwelling robot residing within the ruminant gut would likely revolutionize metabol ...
DOI:
10.1016/j.atech.2022.100109
CHORUS:
10.1016/j.atech.2022.100109

14. Optical and portable equipment for characterizing soil roughness

Author:
Bianca Batista Barreto; Elisângela Ribeiro; Fernando Pujaico Rivera; Roberto Alves Braga Junior; Bernardo Cândido; Blair McKenzie
Source:
Smart agricultural technology 2023 v.3 pp. 100062
ISSN:
2772-3755
Subject:
barns; composts; lighting; optical equipment; portable equipment; roughness; soil; surface roughness; vision
Abstract:
... Knowledge of surface roughness with the consequent presence of crust in the soil is important information for the rational management of environmental resources. Soil surface roughness can be determined by contact and noncontact methods. Contact methods have lower values and precision than noncontact methods. Therefore, the objective of this study was to develop low-cost, portable and robust optic ...
DOI:
10.1016/j.atech.2022.100062

15. Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree

Author:
Ndiye M. Kebonye; Prince C. Agyeman; James K.M. Biney
Source:
Smart agricultural technology 2023 v.3 pp. 100106
ISSN:
2772-3755
Subject:
algorithms; databases; decision support systems; model validation; models; regression analysis; soil organic carbon; Czech Republic
Abstract:
... There are relatively few studies that explicitly evaluate the performance of machine learning algorithms (MLAs) such as decision trees while varying conditions like data splitting strategies and feature selection methods in digital soil mapping (DSM). Since several more powerful black-box models such as Random Forest (RF) exist, regular models like the Classification and Regression Tree (CART) are ...
DOI:
10.1016/j.atech.2022.100106

16. Plant water stress monitoring and control system

Author:
Daudi S. Simbeye; Mbazingwa E. Mkiramweni; Bilal Karaman; Sezai Taskin
Source:
Smart agricultural technology 2023 v.3 pp. 100066
ISSN:
2772-3755
Subject:
Solanum lycopersicum var. lycopersicum; acoustics; carbon dioxide; cohesion; data collection; drought; energy; greenhouses; humidity; irrigation; liquids; mathematical models; soil; technology; temperature; tomatoes; transpiration; water conservation; water flow; water requirement; water stress
Abstract:
... Precision irrigation is an important component of the water-saving approaches in agriculture and is one of the methods for increasing water-saving efficiency. Detecting water stress in crops accurately is the basis of precision irrigation techniques. In this study, the crop water stress monitoring system was developed to detect the acoustic emission (AE) of plants, associating the three factors of ...
DOI:
10.1016/j.atech.2022.100066

17. Prediction of morpho-physiological traits in sugarcane using aerial imagery and machine learning

Author:
Chiranjibi Poudyal; Hardev Sandhu; Yiannis Ampatzidis; Dennis Calvin Odero; Orlando Coto Arbelo; Ronald H. Cherry; Lucas Fideles Costa
Source:
Smart agricultural technology 2023 v.3 pp. 100104
ISSN:
2772-3755
Subject:
Saccharum; cultivars; leaf area index; models; plant analysis; plant height; prediction; regression analysis; remote sensing; soil; sugarcane; Florida
Abstract:
... Changes in morpho-physiological traits are important for biotic or abiotic stresses in sugarcane (Saccharum spp. interspecific hybrids). Ground measurements of such traits are labor-intensive and time-consuming. Therefore, predicting them using aerial imagery can be important for detecting stress and timely management. In this study, ground data and aerial imagery were collected from plant cane an ...
DOI:
10.1016/j.atech.2022.100104
CHORUS:
10.1016/j.atech.2022.100104

18. Present status and challenges of fodder production in controlled environments: A review

Author:
Md Shamim Ahamed; Muhammad Sultan; Redmond R. Shamshiri; Md Mostafizar Rahman; Muhammad Aleem; Siva K. Balasundram
Source:
Smart agricultural technology 2023 v.3 pp. 100080
ISSN:
2772-3755
Subject:
adverse effects; agricultural land; biomass production; capital; climate; environmental sustainability; food production; forage; greenhouses; hydroponics; industry; livestock production; relative humidity; temperature; water conservation
Abstract:
... Hydroponic fodder production in controlled environment (CE) settings have gained more focus in recent years due to the shortage of agricultural land for food production and the adverse effect of climate changes. However, the operation costs and dry matter issues are the major concerns for the sustainability of fodder production in the CE. This study provides a comprehensive literature review on te ...
DOI:
10.1016/j.atech.2022.100080

19. Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique

Author:
Bezuayehu Gutema Asefa; Fikadu Tsige; Mina Mehdi; Tamirat Kore; Aschalew Lakew
Source:
Smart agricultural technology 2023 v.3 pp. 100097
ISSN:
2772-3755
Subject:
Eragrostis tef; agroecology; discriminant analysis; food supply chain; models; multivariate analysis; prediction
Abstract:
... Varieties of a single crop type may vary in several attributes affecting the choice at different spots of the food supply chain. This paper demonstrates a rapid classification of ten tef [Eragrostis tef (Zucc.) Trotter] grain varieties based on image processing and multivariate data analysis. Extreme Gradient Boosted Tree Discriminant Analysis (EGBDA) was applied for the variety-based classificati ...
DOI:
10.1016/j.atech.2022.100097

20. Semi-supervised machine learning analysis of crop color for autonomous irrigation

Author:
Arya Tschand
Source:
Smart agricultural technology 2023 v.3 pp. 100116
ISSN:
2772-3755
Subject:
color; computer vision; data collection; electronic circuits; infrastructure; irrigated farming; irrigation rates; irrigation systems; memory; people; raspberries; satellites; water flow; water shortages
Abstract:
... Agricultural irrigation wastage, especially from inefficient infrastructure in developing nations, is among the leading causes of the global water shortage epidemic, which affects an estimated 2.8 billion people worldwide. In this project, a three-component cloudless internet-of-things (IoT)-based irrigation network is designed and constructed to affordably integrate into existing irrigation infra ...
DOI:
10.1016/j.atech.2022.100116