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Special Issue "Remote Sensing Technologies, Crop Yield, Soil and Weather Data Integration in Digital Agriculture"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 13 October 2023 | Viewed by 10789

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Special Issue Editors

Dr. Abid Ali

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Guest Editor

Department of Agricultural and Food Sciences-DISTAL, University of Bologna, Viale Fanin 44, North Wing, 40127 Bologna, Italy
Interests: foliar application of nutrients; organic farming; quantum GIS; ArcGIS; landsat vegetation indices; ECa directed to soil sampling technique; geostatistical analysis; precision agriculture; remote sensing

Dr. Flavio Lupia

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Guest Editor

CREA Research Centre for Agricultural Policies and Bioeconomy, Via Po 14, 00198 Rome, Italy
Interests: GIS; remote sensing; open data; agricultural statistics; agri-environment
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Dr. Bahattin Akdemir

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Guest Editor

Tekirdag Namik Kemal University, Faculty of Agriculture, Department of Biosystems Engineering, 59030 Tekirdag, Turkey
Interests: precision agriculture; precision horticulture; variable rate application; spatial variability; yield mapping; sensors in agriculture; automatic steering; agricultural machinery; tractors

Dr. Zhongxin Chen

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Guest Editor

Senior IT Officer, IT Service Division (CSI), Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy
Interests: remote sensing applications in agriculture; data assimilation; agro-geoinformatics
Special Issues, Collections and Topics in MDPI journals

Dr. Dariusz Gozdowski

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Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences—SGGW, 159 Nowoursynowska St., 02-776 Warsaw, Poland
Interests: statisticsbio; statistics; GIS; geostatistics; precision agriculture

Special Issue Information

Dear Colleagues,

The current global food and agriculture system is facing major global challenges including climate change, population growth, environmental degradation biodiversity loss and natural resources depletion. It is recognized that agricultural digitalization might be one of the approaches that can help to counterbalance the current situation with the help of remote sensing and other technologies producing a huge amount of relevant data at parcel, farm and regional levels.

Today, we can model the crop yield performances, quality of the agricultural product and environmental effects of agricultural input usage in the spatio-temporal dimension by exploiting the remotely and proximal collectable data and by analyzing the relationships among crop, soil, weather and farm management practices. Several approaches are being developed to allow the precise management of farm resources as function of the within-field variability enacting a relevant leap compared to the traditional agricultural practices. However, the implementation of precision agriculture practices faces the challenge due to the diversity of factors which impact the crop yields and quality such as size of agricultural lands and variability of topography, soil, moisture and microclimatic conditions etc.

In this special issue; we focus on the state-of-art research on digital agriculture enabled by integrating remote, proximal and ground sensing technologies with crop, soil and weather data in search of a sustainable use of farm inputs. Innovative approaches are solicited on measurement, management/integration and use of data established by technologies for better understanding and managing the within-field variability and its relationship with remote, proximally and ground-sensed data. We invite you to submit reviews, case studies, or research articles for that focus on scientific methods, technological tools, and innovative statistical analyses, to capture the current advancements and fostering an open discussion on the future perspectives on the smart exploitation of spatial data integration in agriculture.

Dr. Abid Ali
Dr. Flavio Lupia
Dr. Bahattin Akdemir
Dr. Zhongxin Chen
Dr. Dariusz Gozdowski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Spatio-temporal soil/crop/product quality variability
Spectral vegetation indices
Proximal soil and crop sensing
Soil spatial variability
Weather data and irrigation
Geostatistical analysis of soil and crop variability
Site-specific crop management
Precision farming
Digital agriculture

Published Papers (5 papers)

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Research

Open AccessArticle

Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data

and

Remote Sens. 2023, 15(10), 2511; https://doi.org/10.3390/rs15102511 - 10 May 2023

Viewed by 918

Abstract

Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been considered in modeling. The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading stage, along with weather data. The effects of the CNN architectures, layer depths, and weather data integration methods on the prediction accuracy were evaluated. Overall, the multimodal deep learning model integrating UAV-based multispectral imagery and weather data had the potential to develop more precise rice yield predictions. The best models were those trained with weekly weather data. A simple CNN feature extractor for UAV-based multispectral image input data might be sufficient to predict crop yields accurately. However, the spatial patterns of the predicted yield maps differed from model to model, although the prediction accuracy was almost the same. The results indicated that not only the prediction accuracies, but also the robustness of within-field yield predictions, should be assessed in further studies. Full article

(This article belongs to the Special Issue Remote Sensing Technologies, Crop Yield, Soil and Weather Data Integration in Digital Agriculture)

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Open AccessArticle

Forage Mass Estimation in Silvopastoral and Full Sun Systems: Evaluation through Proximal Remote Sensing Applied to the SAFER Model

Samira Luns Hatum de Almeida

Jarlyson Brunno Costa Souza

Sandra Furlan Nogueira

José Ricardo Macedo Pezzopane

Antônio Heriberto de Castro Teixeira

Cristiam Bosi

Marcos Adami

Cristiano Zerbato

Alberto Carlos de Campos Bernardi

Gustavo Bayma

and

Rouverson Pereira da Silva

Remote Sens. 2023, 15(3), 815; https://doi.org/10.3390/rs15030815 - 31 Jan 2023

Viewed by 800

Abstract

The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method. Full article

(This article belongs to the Special Issue Remote Sensing Technologies, Crop Yield, Soil and Weather Data Integration in Digital Agriculture)

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Open AccessArticle

A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt

Shahid Nawaz Khan

Dapeng Li

and

Maitiniyazi Maimaitijiang

Remote Sens. 2022, 14(12), 2843; https://doi.org/10.3390/rs14122843 - 14 Jun 2022

Cited by 6 | Viewed by 3335

Abstract

Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop yield using different types of variables. In this study, we propose using the Geographically Weighted Random Forest Regression (GWRFR) approach to improve crop yield prediction at the county level in the US Corn Belt. We trained the GWRFR and five other popular machine learning algorithms (Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)) with the following different sets of features: (1) full length features; (2) vegetation indices; (3) gross primary production (GPP); (4) climate data; and (5) soil data. We compared the results of the GWRFR with those of the other five models. The results show that the GWRFR with full length features (R² = 0.90 and RMSE = 0.764 MT/ha) outperforms other machine learning algorithms. For individual categories of features such as GPP, vegetation indices, climate, and soil features, the GWRFR also outperforms other models. The Moran’s I value of the residuals generated by GWRFR is smaller than that of other models, which shows that GWRFR can better address the spatial non-stationarity issue. The proposed method in this article can also be potentially used to improve yield prediction for other types of crops in other regions. Full article

(This article belongs to the Special Issue Remote Sensing Technologies, Crop Yield, Soil and Weather Data Integration in Digital Agriculture)

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Open AccessArticle

Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties

Roomesh Kumar Jena

Siladitya Bandyopadhyay

Upendra Kumar Pradhan

Pravash Chandra Moharana

Sundaram Ramachandran

Bachaspati Das

Surendra Kumar Singh

Sanjay Kumar Ray

Amnah Mohammed Alsuhaibani

Ahmed Gaber

and

Akbar Hossain

Remote Sens. 2022, 14(9), 2101; https://doi.org/10.3390/rs14092101 - 27 Apr 2022

Cited by 1 | Viewed by 1390

Abstract

Defining nutrient management zones (MZs) is crucial for the implementation of site-specific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise representative soil samples (0–25 cm depth) were collected from the study area (1615 km²). The soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K) and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives were used as environmental covariates. The coefficient of determination (R²) and root mean square error were used to evaluate the model’s performance. The predicted soil parameters were accurate, and regression kriging identified the highest variability for the majority of the soil variables. Further, to define the management zones, the geographically weighted principal component analysis and possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters were identified by employing fuzzy performance index and normalized classification entropy. The management zones were constructed considering the total pixel points of 30 m spatial resolution (17, 86,985 data points). The area was divided into four distinct zones, which could be differently managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4 (0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient management, but would also help the policymakers to bring sustainable changes in the north-eastern Himalayan region of India. Full article

(This article belongs to the Special Issue Remote Sensing Technologies, Crop Yield, Soil and Weather Data Integration in Digital Agriculture)

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Open AccessArticle

Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

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Remote Sens. 2021, 13(19), 4000; https://doi.org/10.3390/rs13194000 - 06 Oct 2021

Cited by 13 | Viewed by 2342

Abstract

Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R², RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture. Full article

(This article belongs to the Special Issue Remote Sensing Technologies, Crop Yield, Soil and Weather Data Integration in Digital Agriculture)

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