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Crop Yield Prediction in Precision Agriculture

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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 53912

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Prof. Dr. Miklós Neményi

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Guest Editor

Member of the Hungarian Academy of Sciences and Professor Emeritus, Széchenyi István University, Faculty of Agricultural and Food Sciences, H-9200 Mosonmagyaróvár, Vár tér 2
Interests: sustainable agriculture; crop production; plant nutrition; crop management; precision agriculture; IoT and AI in crop management

Dr. Anikó Nyéki

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Guest Editor

Albert Kázmér Faculty in Mosonmagyaróvár, Department of Biological Systems and Precision Technology, Széchenyi University, Vár 2, 9200 Mosonmagyaróvár, Hungary
Interests: plant physiological decision support models; big data; digitalisation; IoT in precision farming.
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Dear Colleagues,

Crop yield prediction is one of the challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. The prediction of crop yield is based on soil, meteorological, environmental, and crop parameters. Decision support models are broadly used to extract significant crop features for prediction. Precision agriculture focuses on monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact.

Crop yield simulations help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, the impact of crop yield variability, and other field conditions over the growing season.

Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using "simpler" statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. The latter has the advantage of being able to handle many parameters indefinitely in time and space, i.e., big data databases created using precision management tools and data collection capabilities can be used in the areas of the meteorology, technology, and soil-related information, including characterizing different plant species.

This Special Issue aims to discuss various yield prediction methods, the adaptation of big data, and the use of interseason databases from different platforms in crop yield forecast. Studies focused on applications regarding prediction methods, data fusion, and the adaptation of big data are invited for submission.

Prof. Dr. Miklós Neményi
Dr. Anikó Nyéki
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

crop models
artificial intelligence
deep learning and machine learning
big data
database of IoT
remote sensing
data fusion
Interseason forecast
sustainable crop production (prediction of water and nutrient deficiencies)
yield influencing variables

Published Papers (10 papers)

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Editorial

Jump to: Research, Review

4 pages, 214 KiB

Open AccessEditorial

Crop Yield Prediction in Precision Agriculture

by Anikó Nyéki and Miklós Neményi

Agronomy 2022, 12(10), 2460; https://doi.org/10.3390/agronomy12102460 - 11 Oct 2022

Cited by 13 | Viewed by 3660

Abstract

Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

Research

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14 pages, 14721 KiB

Open AccessArticle

Pumpkin Yield Estimation Using Images from a UAV

by Henrik Skov Midtiby and Elżbieta Pastucha

Agronomy 2022, 12(4), 964; https://doi.org/10.3390/agronomy12040964 - 16 Apr 2022

Cited by 6 | Viewed by 2274

Abstract

The paper presents a pumpkin yield estimation method using images acquired by a UAV. The processing pipeline is fully automated. It consists of orthomosaic generation, a color model collection using a random subset of the data, color segmentation, and finally counting of pumpkin blobs together with assessing the number of pumpkins in each blob. The algorithm was validated by a manual check of 5% of each tested dataset. The precision value ranges between 0.959 and 0.996, recall between 0.971 and 0.987, and F1 score falls between 0.971 and 0.988. This proves the very high efficiency of the processing workflow and its potential value to farmers. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

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16 pages, 2834 KiB

Open AccessArticle

Using the CERES-Maize Model to Simulate Crop Yield in a Long-Term Field Experiment in Hungary

by Annabella Zelenák, Atala Szabó, János Nagy and Anikó Nyéki

Agronomy 2022, 12(4), 785; https://doi.org/10.3390/agronomy12040785 - 24 Mar 2022

Cited by 3 | Viewed by 2417

Abstract

Precision crop production requires accurate yield prediction and nitrogen management. Crop simulation models may assist in exploring alternative management systems for optimizing water, nutrient and microelements use efficiencies, increasing maize yields. Our objectives were: (i) to access the ability of the CERES-Maize model for predicting yields in long-term experiments in Hungary; (ii) to use the model to assess the effects of different nutrient management (different nitrogen rates—0, 30, 60, 90, 120, and 150 kg ha⁻¹). A long-term experiment conducted in Látókép (Hungary) with various N-fertilizer applications allowed us to predict maize yields under different conditions. The aim of the research is to explore and quantify the effects of ecological, biological, and agronomic factors affecting plant production, as well as to conduct basic science studies on stress factors on plant populations, which are made possible by the 30-year database of long-term experiments and the high level of instrumentation. The model was calibrated with data from a long-term experiment field trial. The purpose of this evaluation was to investigate how the CERES-Maize model simulated the effects of different N treatments in long-term field experiments. Sushi hybrid’s yields increased with elevated N concentrations. The observed yield ranged from 5016 to 14,920 kg ha⁻¹ during the 2016–2020 growing season. The range of simulated data of maize yield was between 6671 and 13,136 kg ha⁻¹. The highest yield was obtained at the 150 kg ha⁻¹ dose in each year studied. In several cases, the DSSAT-CERES Maize model accurately predicted yields, but it was sensitive to seasonal effects and estimated yields inaccurately. Based on the obtained results, the variance analysis significantly affected the year (2016–2020) and nitrogen doses. N fertilizer made a significant difference on yield, but the combination of both predicted and actual yield data did not show any significance. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

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16 pages, 2664 KiB

Open AccessArticle

Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models

by Fathiyya Ulfa, Thomas G. Orton, Yash P. Dang and Neal W. Menzies

Agronomy 2022, 12(2), 384; https://doi.org/10.3390/agronomy12020384 - 03 Feb 2022

Cited by 9 | Viewed by 1946

Abstract

One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

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22 pages, 3549 KiB

Open AccessArticle

Impact of El Niño on Oil Palm Yield in Malaysia

by Jen Feng Khor, Lloyd Ling, Zulkifli Yusop, Wei Lun Tan, Joan Lucille Ling and Eugene Zhen Xiang Soo

Agronomy 2021, 11(11), 2189; https://doi.org/10.3390/agronomy11112189 - 29 Oct 2021

Cited by 12 | Viewed by 6362

Abstract

Oil palm crop yield is sensitive to heat and drought. Therefore, El Niño events affect oil palm production, resulting in price fluctuations of crude palm oil due to global supply shortage. This study developed a new Fresh Fruit Bunch Index (FFBI) model based on the monthly oil palm fresh fruit bunch (FFB) yield data, which correlates directly with the Oceanic Niño Index (ONI) to model the impact of past El Niño events in Malaysia in terms of production and economic losses. FFBI is derived from Malaysian monthly FFB yields from January 1986 to July 2021 in the same way ONI is derived from monthly sea surface temperatures (SST). With FFBI model, the Malaysian oil palm yields are better correlated with ONI and have higher predictive ability. The descriptive and inferential statistical assessments show that the newly proposed FFBI time series model (adjusted R-squared = 0.9312 and residual median = 0.0051) has a better monthly oil palm yield predictive ability than the FFB model (adjusted R-squared = 0.8274 and residual median = 0.0077). The FFBI model also revealed an oil palm under yield concern of the Malaysian oil palm industry in the next thirty-month forecasted period from July 2021 to December 2023. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

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14 pages, 2921 KiB

Open AccessArticle

Stability and Adaptability of Maize Hybrids for Precision Crop Production in a Long-Term Field Experiment in Hungary

by Csaba Bojtor, Seyed Mohammad Nasir Mousavi, Árpád Illés, Adrienn Széles, János Nagy and Csaba L. Marton

Agronomy 2021, 11(11), 2167; https://doi.org/10.3390/agronomy11112167 - 28 Oct 2021

Cited by 16 | Viewed by 2489

Abstract

Sustainability is one of the main components of precision farming that will lead to food security and production resources for current and future generations. The selection of suitable hybrids and fertilizers is among the methods that can directly influence sustainable agriculture and economic efficiency at the farm level, providing accurate site-specific nutrient management strategies for yield maximization. This experiment included two fertilizer sources in ten maize hybrids in four replications for three consecutive years (2018–2020). The experiment was carried out at the Látókép Crop Production Experimental Site of the University of Debrecen, Hungary. The results of the ANOVA showed that genotype, year, and fertilizer levels had various effects on grain yield, oil, protein, and starch content. FAO340 had maximum grain yield on different fertilizers (NPK and N), and FAO350 had maximum protein content. To gain the best performance and maximum yield of maize on protein and oil, FAO350 is recommended for protein and FAO340 for oil content. The parameters of grain yield, oil content, protein content, and starch content affected by NPK fertilizer provide the stability of grain yield parameters. FAO360, FAO420, and FAO320 hybrids had their maximum desirable N fertilizer doses and NPK fertilizer stability in this research. These results indicate that FAO360, FAO420, and FAO330 hybrids had their maximum potential yield in different fertilizer and environmental conditions. Based on this multi-year study, the complete NPK fertilizer with 150 kg/ha nitrogen, 115 kg/ha potassium, 135 kg/ha phosphorus is recommended to be used on maize hybrids. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

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14 pages, 2689 KiB

Open AccessArticle

Effects of NP Fertilizer Placement Depth by Year Interaction on the Number of Maize (Zea mays L.) Plants after Emergence Using the Additive Main Effects and Multiplicative Interaction Model

by Piotr Szulc, Jan Bocianowski, Kamila Nowosad, Henryk Bujak, Waldemar Zielewicz and Barbara Stachowiak

Agronomy 2021, 11(8), 1543; https://doi.org/10.3390/agronomy11081543 - 31 Jul 2021

Cited by 8 | Viewed by 2273

Abstract

Field experiments were carried out at the Department of Agronomy of the Poznań University of Life Sciences to determine the effect of the depth of NP fertilization placement in maize cultivation on the number of plants after emergence. The adopted assumptions were verified based on a six-year field experiment involving four depths of NP fertilizer application (A1—0 cm (broadcast), A2—5 cm (in rows), A3—10 cm (in rows), A4—15 cm (in rows)). The objective of this study was to assess NP fertilizer placement depth, in conjunction with the year, on the number of maize (Zea mays L.) plants after emergence using the additive main effects and multiplicative interaction model. The number of plants after emergence decreased with the depth of NP fertilization in the soil profile, confirming the high dependence of maize on phosphorus and nitrogen availability, as well as greater subsoil loosening during placement. The number of plants after emergence for the experimental NP fertilizer placement depths varied from 7.237 to 8.201 plant m⁻² during six years, with an average of 7.687 plant m⁻². The 61.51% of variation in the total number of plants after emergence was explained by years differences, 23.21% by differences between NP fertilizer placement depths and 4.68% by NP fertilizer placement depths by years interaction. NP fertilizer placement depth 10 cm (A3) was the most stable (ASV = 1.361) in terms of the number of plants after emergence among the studied NP fertilizer placement depths. Assuming that the maize kernels are placed in the soil at a depth of approx. 5 cm, the fertilizer during starter fertilization should be placed 5 cm to the side and below the kernel. Deeper NP fertilizer application in maize cultivation is not recommended. The condition for the use of agriculture progress, represented by localized fertilization, is the simultaneous recognition of the aspects of yielding physiology of new maize varieties and the assessment of their reaction to deeper seed placement during sowing. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

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17 pages, 1994 KiB

Open AccessEditor’s ChoiceArticle

The Application of Multiple Linear Regression and Artificial Neural Network Models for Yield Prediction of Very Early Potato Cultivars before Harvest

by Magdalena Piekutowska, Gniewko Niedbała, Tomasz Piskier, Tomasz Lenartowicz, Krzysztof Pilarski, Tomasz Wojciechowski, Agnieszka A. Pilarska and Aneta Czechowska-Kosacka

Agronomy 2021, 11(5), 885; https://doi.org/10.3390/agronomy11050885 - 30 Apr 2021

Cited by 63 | Viewed by 9875

Abstract

Yield forecasting is a rational and scientific way of predicting future occurrences in agriculture—the level of production effects. Its main purpose is reducing the risk in the decision-making process affecting the yield in terms of quantity and quality. The aim of the following study was to generate a linear and non-linear model to forecast the tuber yield of three very early potato cultivars: Arielle, Riviera, and Viviana. In order to achieve the set goal of the study, data from the period 2010–2017 were collected, coming from official varietal experiments carried out in northern and northwestern Poland. The linear model has been created based on multiple linear regression analysis (MLR), while the non-linear model has been built using artificial neural networks (ANN). The created models can predict the yield of very early potato varieties on 20th June. Agronomic, phytophenological, and meteorological data were used to prepare the models, and the correctness of their operation was verified on the basis of separate sets of data not participating in the construction of the models. For the proper validation of the model, six forecast error metrics were used: i.e., global relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE). As a result of the conducted analyses, the forecast error results for most models did not exceed 15% of MAPE. The predictive neural model NY1 was characterized by better values of quality measures and ex post forecast errors than the regression model RY1. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

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Review

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23 pages, 452 KiB

Open AccessReview

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

by Uzair Ahmad, Abozar Nasirahmadi, Oliver Hensel and Stefano Marino

Agronomy 2022, 12(3), 555; https://doi.org/10.3390/agronomy12030555 - 23 Feb 2022

Cited by 14 | Viewed by 4393

Abstract

Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

27 pages, 2304 KiB

Open AccessEditor’s ChoiceReview

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

by André Barriguinha, Miguel de Castro Neto and Artur Gil

Agronomy 2021, 11(9), 1789; https://doi.org/10.3390/agronomy11091789 - 07 Sep 2021

Cited by 26 | Viewed by 5521

Abstract

Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods. Full article

(This article belongs to the Special Issue Crop Yield Prediction in Precision Agriculture)

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