Agronomy

Research

31 pages, 4645 KiB

Open AccessEditor’s ChoiceArticle

Sustainable Phosphorus Fertilizer Supply Chain Management to Improve Crop Yield and P Use Efficiency Using an Ensemble Heuristic–Metaheuristic Optimization Algorithm

by Mohammad Shokouhifar, Mahnaz Sohrabi, Motahareh Rabbani, Seyyed Mohammad Hadji Molana and Frank Werner

Agronomy 2023, 13(2), 565; https://doi.org/10.3390/agronomy13020565 - 16 Feb 2023

Cited by 22 | Viewed by 3287

Abstract

Phosphorus (P) is the most important substance in inorganic fertilizers used in the agriculture industry. In this study, a multi-product and multi-objective model is presented considering economic and environmental concerns to design a renewable and sustainable P-fertilizer supply chain management (PFSCM) strategy. To [...] Read more.

Phosphorus (P) is the most important substance in inorganic fertilizers used in the agriculture industry. In this study, a multi-product and multi-objective model is presented considering economic and environmental concerns to design a renewable and sustainable P-fertilizer supply chain management (PFSCM) strategy. To handle the complexities of the model, an ensemble heuristic–metaheuristic algorithm utilizing the heuristic information available in the model, the whale optimization algorithm, and a variable neighborhood search (named H-WOA-VNS) is proposed. First, a problem-dependent heuristic is designed to generate a set of near-optimal feasible solutions. These solutions are fed into a population-based whale optimization algorithm which benefits from exploration and exploitation strategies. Finally, the single-solution variable neighborhood search is applied to further improve the quality of the solution using local search operators. The objective function of the algorithm is formulated as a weighted average function to minimize total economic cost while increasing crop yield and P use efficiency. The experimental results for a real case study of the P-fertilizer supply chain confirm the effectiveness of the proposed method in improving the crop yield and P use efficiency by 33% and 27.8%, respectively. The results demonstrate that the proposed H-WOA-VNS algorithm outperforms the Heuristic, WOA, and VNS models in reducing the total objective function value of the PFSCM model by 9.8%, 2.9%, and 4%, respectively. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Modeling, Optimization and Control in Smart Agriculture)

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16 pages, 3038 KiB

Open AccessArticle

Research on Control Strategy of Light and CO₂ in Blueberry Greenhouse Based on Coordinated Optimization Model

by Xinyu Wen, Lihong Xu and Ruihua Wei

Agronomy 2022, 12(12), 2988; https://doi.org/10.3390/agronomy12122988 - 28 Nov 2022

Cited by 3 | Viewed by 2139

Abstract

As essential environmental parameters in the greenhouse, appropriate light and CO₂ will improve agricultural productivity and quality. Although many related studies have been carried out on the intelligent regulation of these environmental factors, the regulation of light and CO₂ is usually [...] Read more.

As essential environmental parameters in the greenhouse, appropriate light and CO₂ will improve agricultural productivity and quality. Although many related studies have been carried out on the intelligent regulation of these environmental factors, the regulation of light and CO₂ is usually controlled separately, and energy consumption is rarely considered. This paper proposed a coordinated control strategy for greenhouse light and CO₂ based on the multi-objective optimization model. Firstly, the experiments on the net photosynthetic rate of blueberry under different temperatures, photon flux density, and CO₂ concentration nesting were carried out to establish a blueberry net photosynthetic rate prediction model based on Support Vector Regression (SVR). Secondly, a model for calculating the energy cost of both light and CO₂ was constructed. Thirdly, taking the maximum net photosynthetic rate and the minimum energy cost as the objective functions, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was leveraged to obtain the Pareto optimal solutions of the target regulation values of light and CO₂ concentration in different temperature ranges. Then, the optimal values were selected based on two different strategies. Finally, the multi-objective optimal control strategy proposed in this paper was compared with both the classical threshold control strategy and the Gaussian curvature maximization control strategy. The results indicated that the strategy which prioritized energy saving could reduce the energy cost by about 22.33% and 19.08%, respectively, under the premise that the net photosynthetic rate was consistent. Meanwhile, the strategy that prioritized production efficiency could increase the net photosynthetic rate by about 8.40% and 4.42%, respectively, with the same energy cost. In conclusion, the proposed multi-objective optimization control can improve the greenhouse climate control performance and reduce cost compared with other mentioned strategies. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Modeling, Optimization and Control in Smart Agriculture)

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15 pages, 1963 KiB

Open AccessEditor’s ChoiceArticle

Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions

by Songtao Ban, Weizhen Liu, Minglu Tian, Qi Wang, Tao Yuan, Qingrui Chang and Linyi Li

Agronomy 2022, 12(11), 2832; https://doi.org/10.3390/agronomy12112832 - 12 Nov 2022

Cited by 24 | Viewed by 3389

Abstract

Estimation of crop biophysical and biochemical characteristics is the key element for crop growth monitoring with remote sensing. With the application of unmanned aerial vehicles (UAV) as a remote sensing platform worldwide, it has become important to develop general estimation models, which can [...] Read more.

Estimation of crop biophysical and biochemical characteristics is the key element for crop growth monitoring with remote sensing. With the application of unmanned aerial vehicles (UAV) as a remote sensing platform worldwide, it has become important to develop general estimation models, which can interpret remote sensing data of crops by different sensors and in different agroclimatic regions into comprehensible agronomy parameters. Leaf chlorophyll content (LCC), which can be measured as a soil plant analysis development (SPAD) value using a SPAD-502 Chlorophyll Meter, is one of the important parameters that are closely related to plant production. This study compared the estimation of rice (Oryza sativa L.) LCC in two different regions (Ningxia and Shanghai) using UAV-based spectral images. For Ningxia, images of rice plots with different nitrogen and biochar application rates were acquired by a 125-band hyperspectral camera from 2016 to 2017, and a total of 180 samples of rice LCC were recorded. For Shanghai, images of rice plots with different nitrogen application rates, straw returning, and crop rotation systems were acquired by a 5-band multispectral camera from 2017 to 2018, and a total of 228 samples of rice LCC were recorded. The spectral features of LCC in each study area were analyzed and the results showed that the rice LCC in both regions had significant correlations with the reflectance at the green, red, and red-edge bands and 8 vegetation indices such as the normalized difference vegetation index (NDVI). The estimation models of LCC were built using the partial least squares regression (PLSR), support vector regression (SVR), and artificial neural network (ANN) methods. The PLSR models tended to be more stable and accurate than the SVR and ANN models when applied in different regions with R² values higher than 0.7 through different validations. The results demonstrated that the rice canopy LCC in different regions, cultivars, and different types of sensor-based data shared similar spectral features and could be estimated by general models. The general models can be implied to a wider geographic extent to accurately quantify rice LCC, which is helpful for growth assessment and production forecasts. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Modeling, Optimization and Control in Smart Agriculture)

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15 pages, 5827 KiB

Open AccessCommunication

Tree Trunk and Obstacle Detection in Apple Orchard Based on Improved YOLOv5s Model

by Fei Su, Yanping Zhao, Yanxia Shi, Dong Zhao, Guanghui Wang, Yinfa Yan, Linlu Zu and Siyuan Chang

Agronomy 2022, 12(10), 2427; https://doi.org/10.3390/agronomy12102427 - 6 Oct 2022

Cited by 14 | Viewed by 2245

Abstract

In this paper, we propose a tree trunk and obstacle detection method in a semistructured apple orchard environment based on improved YOLOv5s, with an aim to improve the real-time detection performance. The improvement includes using the K-means clustering algorithm to calculate anchor frame [...] Read more.

In this paper, we propose a tree trunk and obstacle detection method in a semistructured apple orchard environment based on improved YOLOv5s, with an aim to improve the real-time detection performance. The improvement includes using the K-means clustering algorithm to calculate anchor frame and adding the Squeeze-and-Excitation module and 10% pruning operation to ensure both detection accuracy and speed. Images of apple orchards in different seasons and under different light conditions are collected to better simulate the actual operating environment. The Gradient-weighted Class Activation Map technology is used to visualize the performance of YOLOv5s network with and without improvement to increase interpretability of improved network on detection accuracy. The detected tree trunk can then be used to calculate the traveling route of an orchard carrier platform, where the centroid coordinates of the identified trunk anchor are fitted by the least square method to obtain the endpoint of the next time traveling rout. The mean average precision values of the proposed model in spring, summer, autumn, and winter were 95.61%, 98.37%, 96.53%, and 89.61%, respectively. The model size of the improved model is reduced by 13.6 MB, and the accuracy and average accuracy on the test set are increased by 5.60% and 1.30%, respectively. The average detection time is 33 ms, which meets the requirements of real-time detection of an orchard carrier platform. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Modeling, Optimization and Control in Smart Agriculture)

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13 pages, 3369 KiB

Open AccessArticle

A Deep Learning Model to Predict Evapotranspiration and Relative Humidity for Moisture Control in Tomato Greenhouses

by Dae-Hyun Jung, Taek Sung Lee, KangGeon Kim and Soo Hyun Park

Agronomy 2022, 12(9), 2169; https://doi.org/10.3390/agronomy12092169 - 13 Sep 2022

Cited by 19 | Viewed by 3250

Abstract

The greenhouse industry achieves stable agricultural production worldwide. Various information and communication technology techniques to model and control the environment have been applied as data from environmental sensors and actuators in greenhouses are monitored in real time. The current study designed data-based, deep [...] Read more.

The greenhouse industry achieves stable agricultural production worldwide. Various information and communication technology techniques to model and control the environment have been applied as data from environmental sensors and actuators in greenhouses are monitored in real time. The current study designed data-based, deep learning models for evapotranspiration (ET) and humidity in tomato greenhouses. Using time-series data and applying long short-term memory (LSTM) modeling, an ET prediction model was developed and validated in comparison with the Stanghellini model. Training with 20-day and testing with 3-day data resulted in RMSEs of 0.00317 and 0.00356 kgm⁻² s⁻¹, respectively. The standard error of prediction indicated errors of 5.76 and 6.45% in training and testing, respectively. Variables were used to produce a feature map using a two-dimensional convolution layer which was transferred to a subsequent layer and finally connected with the LSTM structure for modeling. The RMSE in humidity prediction using the test dataset was 2.87, indicating a performance better than conventional RNN-LSTM models. Irrigation plans and humidity control may be more accurately conducted in greenhouse cultivation using this model. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Modeling, Optimization and Control in Smart Agriculture)

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17 pages, 4442 KiB

Open AccessArticle

Spatiotemporal Uncertainty and Sensitivity Analysis of the SIMPLE Model Applied to Common Beans for Semi-Arid Climate of Mexico

by Miguel Servin-Palestina, Irineo L. López-Cruz, Jorge A. Zegbe-Domínguez, Agustín Ruiz-García, Raquel Salazar-Moreno and Guillermo Medina-García

Agronomy 2022, 12(8), 1813; https://doi.org/10.3390/agronomy12081813 - 30 Jul 2022

Cited by 2 | Viewed by 1651

Abstract

Simulation models are used to estimate, forecast, optimize and identify limiting factors and analyze changes in crop production. In order to obtain a functional and reliable mathematical model, it is necessary to know the source of uncertainty and identify the most influential parameters. [...] Read more.

Simulation models are used to estimate, forecast, optimize and identify limiting factors and analyze changes in crop production. In order to obtain a functional and reliable mathematical model, it is necessary to know the source of uncertainty and identify the most influential parameters. This study aimed to carry out an uncertainty analysis (UA) and a global spatiotemporal sensitivity analysis (SA) for the parameters of the SIMPLE model, which uses 13 parameters, has two state variables and uses daily weather data to simulate crop growth and development. A Monte Carlo simulation was performed for the UA, and Sobol’s method was used for the SA. Four automatic weather stations representing the climatic conditions of the different bean-producing areas in Zacatecas, Mexico, and a four-year historical series of each station for irrigated and rainfed common bean crops were analyzed. From the UA the coefficients of variation (CV) for thermal time were 11.49% and 11.47%, for biomass the CV were 47.94% and 37.80% and for yield the CV were 49.52% and 39.70% for irrigated and rainfed beans, respectively. From the SA, the most influential parameters for irrigated beans were Tsum > Swater > Tbase > I50A > Topt and for rainfed beans, Tsum > Tbase > I50A > Topt > Swater, according to indices calculated on biomass and thermal time. In conclusion, UA was able to accurately quantify the uncertainty of the biomass, and SA allowed the identification of the most influential of the parameters of the SIMPLE model applied to a common bean crop. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Modeling, Optimization and Control in Smart Agriculture)

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19 pages, 3985 KiB

Open AccessArticle

Efficiency-Oriented MPC Algorithm for Path Tracking in Autonomous Agricultural Machinery

by Jiahong Xu, Jing Lai, Rui Guo, Xiaoxiao Lu and Lihong Xu

Agronomy 2022, 12(7), 1662; https://doi.org/10.3390/agronomy12071662 - 12 Jul 2022

Cited by 11 | Viewed by 2386

Abstract

Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and [...] Read more.

Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and the lower control layer—such as Nonlinear Model Predictive Control (NMPC)—receives this optimized tracking trajectory first, and then steers the vehicle to track this trajectory with high accuracy. However, this two-layer structure cannot improve the agricultural productivity at the control layer online, which makes the agricultural operation sub-optimal. In this paper, we focus on agricultural machinery operational efficiency, to represent agricultural productivity; in order to realize optimizing control to further improve agricultural machinery operational efficiency, a new path-tracking control algorithm, named Efficiency-oriented Model Predictive Control (EfiMPC), is proposed. EfiMPC is intrinsically a nested structure, which can consider the global performance of the whole system defined in the operational layer—like the agricultural machinery operational efficiency considered in this paper—in the control layer online; thus, the agricultural machinery operational efficiency can be improved during the farming operation. An unreachable tracking point, denoted as the pseudo-point, has been proposed, to indicate the agricultural machinery operational efficiency objective in a receding horizon fashion; EfiMPC can utilize this pseudo-point to realize the optimizing control online. A simulation case study was used to test the superiority of the proposed EfiMPC algorithm, and the results show that, compared with the traditional NMPC algorithm, the agricultural machinery operational efficiency realized by EfiMPC was improved by 8.56%; thus, the effectiveness of the EfiMPC has been demonstrated. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Modeling, Optimization and Control in Smart Agriculture)

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18 pages, 22778 KiB

Open AccessArticle

Phenotype Tracking of Leafy Greens Based on Weakly Supervised Instance Segmentation and Data Association

by Zhuang Qiang, Jingmin Shi and Fanhuai Shi

Agronomy 2022, 12(7), 1567; https://doi.org/10.3390/agronomy12071567 - 29 Jun 2022

Cited by 6 | Viewed by 1789

Abstract

Phenotype analysis of leafy green vegetables in planting environment is the key technology of precision agriculture. In this paper, deep convolutional neural network is employed to conduct instance segmentation of leafy greens by weakly supervised learning based on box-level annotations and Excess Green [...] Read more.

Phenotype analysis of leafy green vegetables in planting environment is the key technology of precision agriculture. In this paper, deep convolutional neural network is employed to conduct instance segmentation of leafy greens by weakly supervised learning based on box-level annotations and Excess Green (ExG) color similarity. Then, weeds are filtered based on area threshold, K-means clustering and time context constraint. Thirdly, leafy greens tracking is achieved by bipartite graph matching based on mask IoU measure. Under the framework of phenotype tracking, some time-context-dependent phenotype analysis tasks such as growth monitoring can be performed. Experiments show that the proposed method can achieve 0.95 F1-score and 76.3 sMOTSA (soft multi-object tracking and segmentation accuracy) by using weakly supervised annotation data. Compared with the fully supervised approach, the proposed method can effectively reduce the requirements for agricultural data annotation, which has more potential in practical applications. Full article

(This article belongs to the Special Issue Intelligent Monitoring, Modeling, Optimization and Control in Smart Agriculture)

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