Fruit yield prediction and estimation in orchards: A state-of-the-art comprehensive review for both direct and indirect methods

He, Leilei; Fang, Wentai; Zhao, Guanao; Wu, Zhenchao; Fu, Longsheng; Li, Rui; Majeed, Yaqoob; Dhupia, Jaspreet Singh

doi:10.1016/j.compag.2022.106812

Cited by 65 publications

(38 citation statements)

References 84 publications

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“…In our study, we obtained MAPE error of 12.48% for XGBoost. Most of the other algorithms did not exceed 20% MAPE, with the exception of ElasticNet, NuSVR, MLP(100) and MLP (10). This indicates a good model fit.…”

Section: Resultsmentioning

confidence: 85%

“…Balanced consumption of these products leads to both reduced energy input on the farm and reduced input of human labour. Finally, a plantation can increase profitability due to lower production costs [9,10]. Yield prediction is also used as a tool when theoretical yields need to be estimated in agricultural damage assessments ( [11]).…”

Section: Introductionmentioning

confidence: 99%

“…In the literature on fruit yield forecasting of orchard crops, some of the most important criteria for dividing yield determination methods include data nature, data type and data source. Thus, the first classification separates them into direct and indirect methods [10]. This classification describes direct methods as yield estimation methods and indirect methods as yield prediction methods, but this is not common nomenclature.…”

Section: Introductionmentioning

confidence: 99%

“…These traits can be divided into several categories. The most important include traits attributed to plants, climate, soil conditions and agrotechnical processes [10]. In the case of data attributed to climate, the most common include meteorological data on historical and current air and soil parameters, such as the amount of natural precipitation and solar activity, i.e., insolation or solar radiation [24].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning is the most widely used method for yield modeling due to the highest accuracy of predictions [10,48]. Obsie et al [11] conducted a study evaluating the importance of bee species composition and weather factors in regulating wild blueberry agroecosystems.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods

et al. 2022

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In this paper, we present a high-accuracy model for blueberry yield prediction, trained using structurally innovative data sets. Blueberries are blooming plants, valued for their antioxidant and anti-inflammatory properties. Yield on the plantations depends on several factors, both internal and external. Predicting the accurate amount of harvest is an important aspect in work planning and storage space selection. Machine learning algorithms are commonly used in such prediction tasks, since they are capable of finding correlations between various factors at play. Overall data were collected from years 2016–2021, and included agronomic, climatic and soil data as well satellite-imaging vegetation data. Additionally, growing periods according to BBCH scale and aggregates were taken into account. After extensive data preprocessing and obtaining cumulative features, a total of 11 models were trained and evaluated. Chosen classifiers were selected from state-of-the-art methods in similar applications. To evaluate the results, Mean Absolute Percentage Error was chosen. It is superior to alternatives, since it takes into account absolute values, negating the risk that opposite variables will cancel out, while the final result outlines percentage difference between the actual value and prediction. Regarding the research presented, the best performing solution proved to be Extreme Gradient Boosting algorithm, with MAPE value equal to 12.48%. This result meets the requirements of practical applications, with sufficient accuracy to improve the overall yield management process. Due to the nature of machine learning methodology, the presented solution can be further improved with annually collected data.

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Section: Resultsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods

et al. 2022

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Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

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Crop Yield Prediction Using Artificial Intelligence and Remote Sensing Methods

Banerjee,

Bharti,

Das

et al. 2024

Advances in Geographical and Environmental Sciences

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Fruit yield prediction and estimation in orchards: A state-of-the-art comprehensive review for both direct and indirect methods

Cited by 65 publications

References 84 publications

Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods

Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

Crop Yield Prediction Using Artificial Intelligence and Remote Sensing Methods

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