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Semi-supervised deep learning and low-cost cameras for the semantic segmentation of natural images in viticulture Precision Agric. (IF 5.385) Pub Date : 2022-06-21 A. Casado-García, J. Heras, A. Milella, R. Marani
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Can nitrogen input mapping from aerial imagery improve nitrous oxide emissions estimates from grazed grassland? Precision Agric. (IF 5.385) Pub Date : 2022-06-21 Juliette Maire, Simon Gibson-Poole, Nicholas Cowan, Dominika Krol, Cathal Somers, Dave S. Reay, Ute Skiba, Robert M. Rees, Gary J. Lanigan, Karl G. Richards
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Greenhouse gas mitigation benefits and profitability of the GreenSeeker Handheld NDVI sensor: evidence from Mexico Precision Agric. (IF 5.385) Pub Date : 2022-06-15 Daniel Lapidus, Marwa E. Salem, Robert H. Beach, Stephanie Zayed, Ivan Ortiz-Monasterio
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Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm Precision Agric. (IF 5.385) Pub Date : 2022-06-15 Ziwen Xie, Song Chen, Guizhen Gao, Hao Li, Xiaoming Wu, Lei Meng, Yuntao Ma
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Canopy defoliation by leaf-cutting ants in eucalyptus plantations inferred by unsupervised machine learning applied to remote sensing Precision Agric. (IF 5.385) Pub Date : 2022-06-15 Alexandre dos Santos, Isabel Carolina de Lima Santos, Jeffersoney Garcia Costa, Zakariyyaa Oumar, Mariane Camargo Bueno, Tarcísio Marcos Macedo Mota Filho, Ronald Zanetti, José Cola Zanuncio
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A novel sampling design considering the local heterogeneity of soil for farm field-level mapping with multiple soil properties Precision Agric. (IF 5.385) Pub Date : 2022-06-15 Yongji Wang, Qingwen Qi, Zhengyi Bao, Lili Wu, Qingling Geng, Jun Wang
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Multispectral images for monitoring the physiological parameters of coffee plants under different treatments against nematodes Precision Agric. (IF 5.385) Pub Date : 2022-06-15 Fernando Vasconcelos Pereira, George Deroco Martins, Bruno Sérgio Vieira, Gleice Aparecida de Assis, Vinicius Silva Werneck Orlando
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A novel plant disease prediction model based on thermal images using modified deep convolutional neural network Precision Agric. (IF 5.385) Pub Date : 2022-06-15 Ishita Bhakta, Santanu Phadikar, Koushik Majumder, Himadri Mukherjee, Arkaprabha Sau
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The need for streamlining precision agriculture data in Africa Precision Agric. (IF 5.385) Pub Date : 2022-06-14 Tegbaru B. Gobezie, Asim Biswas
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The effect of growth stage and plant counting accuracy of maize inbred lines on LAI and biomass prediction Precision Agric. (IF 5.385) Pub Date : 2022-06-09 Yingpu Che, Qing Wang, Long Zhou, Xiqing Wang, Baoguo Li, Yuntao Ma
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Estimation of canopy nitrogen content in winter wheat from Sentinel-2 images for operational agricultural monitoring Precision Agric. (IF 5.385) Pub Date : 2022-06-03 Christian Bossung, Martin Schlerf, Miriam Machwitz
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Bayesian optimal dynamic sampling procedures for on-farm field experimentation Precision Agric. (IF 5.385) Pub Date : 2022-06-03 John N. Ng’ombe, B. Wade Brorsen
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Intelligent robots for fruit harvesting: recent developments and future challenges Precision Agric. (IF 5.385) Pub Date : 2022-06-02 Hongyu Zhou, Xing Wang, Wesley Au, Hanwen Kang, Chao Chen
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Mapping coffee yield with computer vision Precision Agric. (IF 5.385) Pub Date : 2022-06-01 Helizani Couto Bazame, José Paulo Molin, Daniel Althoff, Maurício Martello, Lucas De Paula Corrêdo
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Combining low-cost noisy measurements with expensive accurate measurements to guide precision applications Precision Agric. (IF 5.385) Pub Date : 2022-05-28 Whoi Cho, Abby ShalekBriski, B. Wade Brorsen, Davood Poursina
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Soil-moisture-index spectrum reconstruction improves partial least squares regression of spectral analysis of soil organic carbon Precision Agric. (IF 5.385) Pub Date : 2022-05-28 Lixin Lin, Xixi Liu
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A fast and robust method for plant count in sunflower and maize at different seedling stages using high-resolution UAV RGB imagery Precision Agric. (IF 5.385) Pub Date : 2022-05-29 Yi Bai, Chenwei Nie, Hongwu Wang, Minghan Cheng, Shuaibing Liu, Xun Yu, Mingchao Shao, Zixu Wang, Siyu Wang, Nuremanguli Tuohuti, Lei Shi, Bo Ming, Xiuliang Jin
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Remote sensing inversion of soil organic matter by using the subregion method at the field scale Precision Agric. (IF 5.385) Pub Date : 2022-05-30 Yue Pan, Xinle Zhang, Huanjun Liu, Danqian Wu, Xin Dou, Mengyuan Xu, Yun Jiang
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Forecasting seasonal plot-specific crop coefficient (Kc) protocol for processing tomato using remote sensing, meteorology, and artificial intelligence Precision Agric. (IF 5.385) Pub Date : 2022-05-23 Ran Pelta, Ofer Beeri, Rom Tarshish, Tal Shilo
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Assessing expected utility and profitability to support decision-making for disease control strategies in ornamental heather production Precision Agric. (IF 5.385) Pub Date : 2022-05-22 Marius Ruett, Tobias Dalhaus, Cory Whitney, Eike Luedeling
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Factors leading to spatiotemporal variability of soil moisture and turfgrass quality within sand-capped golf course fairways Precision Agric. (IF 5.385) Pub Date : 2022-05-22 Reagan Hejl, Chase Straw, Benjamin Wherley, Rebecca Bowling, Kevin McInnes
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An assessment of multi-view spectral information from UAV-based color-infrared images for improved estimation of nitrogen nutrition status in winter wheat Precision Agric. (IF 5.385) Pub Date : 2022-05-19 Ning Lu, Yapeng Wu, Hengbiao Zheng, Xia Yao, Yan Zhu, Weixing Cao, Tao Cheng
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Single superphosphate fertilizer distribution among seeder-fertilizer rows Precision Agric. (IF 5.385) Pub Date : 2022-05-19 Luiz Cláudio Garcia, Gaison Sampaio de Lima, Ronaldo Dalzoto, Janaine Ritter, Lucas Aleksander Broniski Brigolla, Thiago Massao Inagaki, Flavia Biassio Riferte, Adriel Ferreira da Fonseca
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Spatial patchiness and association of pests and natural enemies in agro-ecosystems and their application in precision pest management: a review Precision Agric. (IF 5.385) Pub Date : 2022-05-11 Roghaiyeh Karimzadeh, Andrea Sciarretta
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Strategies for monitoring within-field soybean yield using Sentinel-2 Vis-NIR-SWIR spectral bands and machine learning regression methods Precision Agric. (IF 5.385) Pub Date : 2022-04-19 L. G.T. Crusiol, Liang Sun, R. N.R. Sibaldelli, V. Felipe Junior, W. X. Furlaneti, R. Chen, Z. Sun, D. Wuyun, Z. Chen, M. R. Nanni, R. H. Furlanetto, E. Cezar, A. L. Nepomuceno, J. R.B. Farias
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Precision land leveling for sustainable rice production: case studies in Cambodia, Thailand, Philippines, Vietnam, and India Precision Agric. (IF 5.385) Pub Date : 2022-04-14 Nguyen-Van-Hung, Carlito Balingbing, Joseph Sandro, Suryakanta Khandai, Hong Chea, Thanach Songmethakrit, Pyseth Meas, Gerald Hitzler, Walter Zwick, Ladda Viriyangkura, Elmer Bautista, Martin Gummert
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Estimating maize seedling number with UAV RGB images and advanced image processing methods Precision Agric. (IF 5.385) Pub Date : 2022-04-04 Shuaibing Liu, Dameng Yin, Haikuan Feng, Zhenhai Li, Xiaobin Xu, Lei Shi, Xiuliang Jin
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Statistical and machine learning methods for crop yield prediction in the context of precision agriculture Precision Agric. (IF 5.385) Pub Date : 2022-03-30 Hannah Burdett, Christopher Wellen
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Improving soil property maps for precision agriculture in the presence of outliers using covariates Precision Agric. (IF 5.385) Pub Date : 2022-03-21 Maiara Pusch, Alessandro Samuel-Rosa, Agda Loureiro Gonçalves Oliveira, Paulo Sergio Graziano Magalhães, Lucas Rios do Amaral
This study aimed to evaluate the use of multiple covariates in robust geostatistical modeling of soil chemical properties characterized by the presence of outliers. Different spatial prediction methods were compared using data from two agricultural areas located in Brazil´s Southeast: one with rotational grazing and one cultivated with sugarcane. Considering the variable-rate fertilizer prescription
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Impact of soil types on sugarcane development monitored over time by remote sensing Precision Agric. (IF 5.385) Pub Date : 2022-03-18 Merilyn Taynara Accorsi Amorim, Nélida E. Q. Silvero, Henrique Bellinaso, Andrés Maurício Rico Gómez, Lucas T. Greschuk, Lucas Rabelo Campos, José A. M. Demattê
Soil is one of the most important factors for agricultural production. In tropical regions, soil variability is considerable, with the most diverse combinations of physical and chemical characteristics, an influence factor in crop growth and productivity. In this research, the main objective was to identify how soil characteristics and parent material can influence sugarcane development over time using
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Mapping stressed wheat plants by soil aluminum effect using C-band SAR images: implications for plant growth and grain quality Precision Agric. (IF 5.385) Pub Date : 2022-03-14 Mercedes Hernández, Andrés A. Borges, Desiderio Francisco-Bethencourt
Under toxic aluminum (Al) levels in the soil, wheat (Triticum aestivum L.) suffers stress and plant growth is affected. A method for diagnosis of plants is proposed that includes the following as a strategy: to analyze total Al in the soil, employ satellite radar imagery and calculate a vegetation index. The objective of this research, conducted at the field scale, was to explore how radar backscattering
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A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy Precision Agric. (IF 5.385) Pub Date : 2022-03-12 T. S. Breure, S. M. Haefele, J. A. Hannam, R. Corstanje, R. Webster, S. Moreno-Rojas, A. E. Milne
Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that
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Processing of remote sensing information to retrieve leaf area index in barley: a comparison of methods Precision Agric. (IF 5.385) Pub Date : 2022-03-12 Pablo Rosso, Claas Nendel, Nicolas Gilardi, Cosmin Udroiu, Florent Chlebowski
Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded
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Spatiotemporal normalized ratio methodology to evaluate the impact of field-scale variable rate application Precision Agric. (IF 5.385) Pub Date : 2022-03-09 L. Katz, A. Ben-Gal, M. I. Litaor, A. Naor, M. Peres, I. Bahat, Y. Netzer, A. Peeters, V. Alchanatis, Y. Cohen
Wide assimilation of precision agriculture among farmers is currently dependent on the ability to demonstrate its efficiency at the field-scale. Yet, most experiments that compare variable-rate vs uniform application (VRA and UA) are performed in strips, concentrated in a small portion of the field with limited extrapolation to the field scale. A spatiotemporal normalized ratio (STNR) methodology is
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Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes Precision Agric. (IF 5.385) Pub Date : 2022-03-05 Juan Miguel Ramírez-Cuesta, Simona Consoli, Domenico Longo, Giuseppe Longo-Minnolo, Diego S. Intrigliolo, Daniela Vanella
Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological
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The adoption of precision agriculture enabling technologies in Swiss outdoor vegetable production: a Delphi study Precision Agric. (IF 5.385) Pub Date : 2022-03-04 Jeanine Ammann, Christina Umstätter, Nadja El Benni
Digital technologies are a promising means to tackle the increasing global challenges (e.g., climate change, water pollution, soil degradation) and revolutionising agricultural production. The current research used a two-stage Delphi study with 34 experts from various domains, including production, advisory and research, to identify the key drivers and barriers, the most promising technologies and
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Design considerations of variable rate liquid fertilizer applicator for mature oil palm trees Precision Agric. (IF 5.385) Pub Date : 2022-03-02 Muhammad Yamin, Wan Ishak bin Wan Ismail, Samsuzana Abd Aziz, Muhamad Saufi bin Mohd Kassim, Farah Naz Akbar, Muhammad Ibrahim
This research study focuses on the design of variable rate liquid fertilizer applicator which can measure the NPK status of soil and applies N, P and K nutrients simultaneously at separate variable rates around the oil palm trees in the form of aqueous solutions of straight fertilizers having single nutrient. A fertilizer flow control and spray system was designed to apply liquid fertilizer around
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Economic potential of site-specific pesticide application scenarios with direct injection and automatic application assistant in northern Germany Precision Agric. (IF 5.385) Pub Date : 2022-03-02 Sandra Rajmis, Isabella Karpinski, Jan-Philip Pohl, Marco Herrmann, Hella Kehlenbeck
A growing and promising sector of precision agriculture is the site-specific application of pesticides, having a high potential for reductions in pesticide use. Within the research project ‘AssSys’, site-specific pesticide applications with a direct injection sprayer system and an automatic application assistant were evaluated economically with respect to herbicide and fungicide applications. The application
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Citrus fruits maturity detection in natural environments based on convolutional neural networks and visual saliency map Precision Agric. (IF 5.385) Pub Date : 2022-03-01 Shumian Chen, Juntao Xiong, Jingmian Jiao, Zhiming Xie, Zhaowei Huo, Wenxin Hu
Citrus fruits do not ripen at the same time in natural environments and exhibit different maturity stages on trees, hence it is necessary to realize selective harvesting of citrus picking robots. The visual attention mechanism reveals a physiological phenomenon that human eyes usually focus on a region that is salient from its surround. The degree to which a region contrasts with its surround is called
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Economically targeting conservation practices to optimize conservation and net revenue using precision agriculture tools Precision Agric. (IF 5.385) Pub Date : 2022-03-01 Nick Meng, Mark D. McConnell, L. Wes Burger
Federal conservation programs can target conservation assistance to the most vulnerable lands by stimulating the adoption of innovative technology including precision agriculture. Productivity and vulnerability vary within fields suggesting conservation programs could be targeted to marginal field regions potentially increasing the whole-field net revenue. Decision-support tools (DST) have been proposed
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Spatial variability of leaf macronutrient concentration and fruit production of an Arabica coffee plantation using two sampling densities Precision Agric. (IF 5.385) Pub Date : 2022-02-23 Gabriel Fernandes Pinto Ferreira, Odair Lacerda Lemos, Rogério Peres Soratto, Marcos José Perdoná
The nutritional and productive attributes of Arabica coffee (Coffea arabica L.) can vary spatially within cultivated areas. Precision farming techniques applied to coffee plantations can diagnose this spatial variability and propose solutions to correct this unevenness. The objective of this study was to characterize the distribution and spatial dependence of leaf macronutrient concentration and fruit
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A review of methods to evaluate crop model performance at multiple and changing spatial scales Precision Agric. (IF 5.385) Pub Date : 2022-02-21 Daniel Pasquel, Sébastien Roux, Jonathan Richetti, Davide Cammarano, Bruno Tisseyre, James A. Taylor
Crop models are useful tools because they can help understand many complex processes by simulating them. They are mainly designed at a specific spatial scale, the field. But with the new spatial data being made available in modern agriculture, they are being more and more applied at multiple and changing scales. These applications range from typically at broader scales, to perform regional or national
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Precisely forecasting population dynamics of agricultural pests based on an interval type-2 fuzzy logic system: case study for oriental fruit flies and the tobacco cutworms Precision Agric. (IF 5.385) Pub Date : 2022-02-18 Joe-Air Jiang, Chih-Hao Syue, Chien-Hao Wang, Min-Sheng Liao, Jiann-Shing Shieh, Jen-Cheng Wang
Traditional pest control approaches rely mostly on the experience of farmers, which may not be effective due to lack of scientific information regarding the environment where crops grow. Farmers can initiate a more effective integrated pest management program when precise and quantified results of forecasting pest population outbreaks are provided. Previous studies generally utilize long-term data
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Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery Precision Agric. (IF 5.385) Pub Date : 2022-02-11 Aijing Feng, Jianfeng Zhou, Earl D. Vories, Kenneth A. Sudduth
Quantification of interactions of soil conditions, plant available water and weather conditions on crop development and production is the key for optimizing field management to achieve optimal production. The goal of this study was to quantify the effects of soil and weather conditions on cotton development and production using temporal aerial imagery data, weather and soil apparent electrical conductivity
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Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting Precision Agric. (IF 5.385) Pub Date : 2022-02-09 Gustavo Willam Pereira, Domingos Sárvio Magalhães Valente, Daniel Marçal de Queiroz, Nerilson Terra Santos, Elpídio Inácio Fernandes-Filho
Kriging has been shown to be the best interpolator to interpolate maps in precision agriculture. However, Kriging requires a high number of sampling points to generate accurate maps. Recently, machine learning (ML) techniques have shown the potential to produce maps with a lower number of sampling points. In addition, using ML map generation can be automated and use much more feature information to
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An improved approach to estimate ratoon rice aboveground biomass by integrating UAV-based spectral, textural and structural features Precision Agric. (IF 5.385) Pub Date : 2022-02-09 Le Xu, Longfei Zhou, Ran Meng, Feng Zhao, Zhengang Lv, Binyuan Xu, Linglin Zeng, Xing Yu, Shaobing Peng
Ratoon rice production has been an emerging cropping system to increase food quality and productivity worldwide. Efficient monitoring of ratoon rice aboveground biomass (AGB) over large areas is valuable for precision agriculture, as AGB is closely related to crop grain yield and quality. Unmanned aerial vehicle (UAV) remote sensing has opened an unprecedented opportunity to efficiently monitor crop
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Optimal vision-based guidance row locating for autonomous agricultural machines Precision Agric. (IF 5.385) Pub Date : 2022-02-03 Piyanun Ruangurai, Matthew N. Dailey, Mongkol Ekpanyapong, Peeyush Soni
With the rapid advances in precision agriculture technology, machine vision is emerging as a means to obtain accurate spatial information for a control system at relatively attractive cost. Besides being affordable and data rich, vision sensors offer precise local or relative information, which can complement global sensors such as global navigation satellite systems (GNSS). The research reported on
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Field methods for making productivity classes for site-specific management of wheat Precision Agric. (IF 5.385) Pub Date : 2022-02-02 Marcelo José López de Sabando, Martín Diaz-Zorita
Reducing the decision-making unit to classes within fields can improve yields, efficiency in the use of nutrients and profitability of crops. The objectives were to compare methods for class delimitation in wheat (Triticum aestivum L.) crops based on apparent productivity levels and establish similarities among them in terms of spatial overlapping, productive attributes and the use of nitrogen. In
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Estimating litchi flower number using a multicolumn convolutional neural network based on a density map Precision Agric. (IF 5.385) Pub Date : 2022-01-31 Jiaquan Lin, Jun Li, Zhou Yang, Huazhong Lu, Yunhe Ding, Huajun Cui
To ensure litchi fruit yield and quality, reasonable blooming period management such as flower thinning is required during the early flowering period. A combination of the number of litchi flowers and their density map can provide a reference for blooming period management decisions during the flowering period. Flowering intensity is currently largely estimated manually by humans observing the trees
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Improved piezoelectric grain cleaning loss sensor based on adaptive neuro-fuzzy inference system Precision Agric. (IF 5.385) Pub Date : 2022-01-29 Mingzhi Jin, Zhan Zhao, Shuren Chen, Junyi Chen
Grain cleaning loss rate is an important performance index of combine harvesters which needs to be measured in real time during the harvesting operation. To improve the measurement accuracy and range, a grain loss sensor based on piezoelectric effect and adaptive neuro-fuzzy inference system (ANFIS) was proposed. A piezoelectric ceramic was fixed on the bottom of a thin sensitive plate to detect grain
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Do water dynamics and land use in riparian areas change the spatial pattern of physical–mechanical properties of a Cambisol? Precision Agric. (IF 5.385) Pub Date : 2022-01-29 Reginaldo Barboza da Silva, Piero Iori, Rose Luiza Moraes Tavares, Zigomar Menezes de Souza, Camila Cassante de Lima, Francisca Alcivânia de Melo Silva, Marília de Souza Bento
Changes in land use in riverside ecosystems added to the flood and ebb regimes of rivers modify the spatial variability of the physical–mechanical attributes of soil, making it more susceptible to degradation processes, especially during the rainy season. The aim of this study was to evaluate the impact of the use of a Eutric Cambisol, which was cultivated with banana trees, on the pattern of spatial
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Medium-resolution multispectral satellite imagery in precision agriculture: mapping precision canola (Brassica napus L.) yield using Sentinel-2 time series Precision Agric. (IF 5.385) Pub Date : 2022-01-29 Lan H. Nguyen, Samuel Robinson, Paul Galpern
Remote sensing imagery has been a key data source for precision agriculture. However, high-resolution and/or hyperspectral imagery have typically been favored for their greater information content. This study aims to demonstrate the capability of medium-resolution imagery in precision agriculture by developing an example of canola yield mapping using Sentinel-2 data in central Alberta. Two simple empirical
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Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach Precision Agric. (IF 5.385) Pub Date : 2022-01-23 Jiang, Jiale, Johansen, Kasper, Stanschewski, Clara S., Wellman, Gordon, Mousa, Magdi A. A., Fiene, Gabriele M., Asiry, Khalid A., Tester, Mark, McCabe, Matthew F.
Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for
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Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages Precision Agric. (IF 5.385) Pub Date : 2022-01-11 Dong, Rui, Miao, Yuxin, Wang, Xinbing, Yuan, Fei, Kusnierek, Krzysztof
Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea mays L.) N status indicators across growth stages; (2)
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Pedology-based management class establishment: a study case in Brazilian coffee crops Precision Agric. (IF 5.385) Pub Date : 2022-01-05 Gonçalves, Mariana Gabriele Marcolino, Avalos, Fabio Arnaldo Pomar, dos Reis, Josimar Vieira, Costa, Milton Verdade, Silva, Sérgio Henrique Godinho, Poggere, Giovana Clarice, Curi, Nilton, de Menezes, Michele Duarte
This work proposes an approach for establishing coffee management classes mainly supported by pedological information (soil survey) and land parcels, taking into account peculiarities of Brazilian coffee crops (land parcels already implemented with different crop ages, cultivars and density) and inspired by some management zone concepts. Two initial datasets were used based on soil survey and/or coffee
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Spatial patterns of soil microbial communities and implications for precision soil management at the field scale Precision Agric. (IF 5.385) Pub Date : 2022-01-04 Neupane, Jasmine, Guo, Wenxuan, Cao, Guofeng, Zhang, Fangyuan, Slaughter, Lindsey, Deb, Sanjit
Understanding the spatial patterns of soil microbial communities and influencing factors is a prerequisite for soil health assessments and site-specific management to improve crop production. However, soil microbial community structure at the field scale is complicated by the interactions among topography and soil properties. The objectives of this study were to (1) characterize the spatial variability
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Determining nitrogen deficiencies for maize using various remote sensing indices Precision Agric. (IF 5.385) Pub Date : 2022-01-01 Burns, Brayden W., Green, V. Steven, Hashem, Ahmed A., Massey, Joseph H., Shew, Aaron M., Adviento-Borbe, M. Arlene A., Milad, Mohamed
Determining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized
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Impact of in-field soil heterogeneity on biomass and yield of winter triticale in an intensively cropped hummocky landscape under temperate climate conditions Precision Agric. (IF 5.385) Pub Date : 2021-12-11 Habib-ur-Rahman, Muhammad, Raza, Ahsan, Ahrends, Hella Ellen, Hüging, Hubert, Gaiser, Thomas
Crop cultivation provides ecosystem services on increasingly large fields. However, the effects of in-field spatial heterogeneity on crop yields, in particular triticale, have rarely been considered. The study assess the effects of in-field soil heterogeneity and elevation on triticale grown in an intensively cropped hummocky landscape. The field was classified into three soil classes: C1, C2, and
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Correction to: Identifying crop yield gaps with site- and season-specific data-driven models of yield potential Precision Agric. (IF 5.385) Pub Date : 2021-12-02 Patrick Filippi,Brett M. Whelan,R. Willem Vervoort,Thomas F. A. Bishop
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Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning Precision Agric. (IF 5.385) Pub Date : 2021-11-17 Costa, Lucas, Kunwar, Sudip, Ampatzidis, Yiannis, Albrecht, Ute
Nutrient assessment of plants, a key aspect of agricultural crop management and varietal development programs, traditionally is time demanding and labor-intensive. This study proposes a novel methodology to determine leaf nutrient concentrations of citrus trees by using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence (AI). The study was conducted in four different citrus