Advanced Technologies in Precision Processing and Modeling in Agriculture

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 35657

Special Issue Editor


E-Mail Website
Guest Editor
Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob 4 Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 5 8499000 Midreshet Ben-Gurion, Israel
Interests: soil physics; soil chemistry; crop modelling; remote sensing

Special Issue Information

Dear colleagues,

The agricultural revolution, which began about 10,000 years ago and accelerated global history, was followed by the scientific revolution, which began about 500 years ago and accelerated history further. The scientific revolution provided a start to another development, namely the precision agriculture (PA) revolution, which brought about the next stage in agricultural development. PA has been carried out by growers since the early days of farming. The aim was always to increase yield and profit, but the concept of modern PA has been driven forward by changes in technology. The management unit increased from smallholders (farms smaller than 2 ha) to medium and large farming systems. The introduction of GIS, GPS, and sophisticated sensors that characterized the PA discipline in the late 20th century improved productivity, but this increased productivity is still insufficient to feed the 9.7 billion people predicted for the world by the year 2050. This increase, according to FAO, needs to be met through a 70% rise in agricultural production. It must be borne in mind that 85% of word's food production comes from smallholders (especially in the Far East). China alone account for 193 million small farms (less than 2 ha) or about 44% of the world’s 441 million small farms. Moreover, the trend is of declining farm size over time. Farm size in China decreased from 0.56 hectares in 1980 to 0.4 hectares in 1999 (Fan and Chan-Kang, 2003); therefore; small farms must be one of the major target groups of the next PA generation, which is smart agriculture (SA). Many use the term SA as a synonym for PA, but to be more precise, the SA generation belongs to the 21st century while PA belongs to the 20th. The difference between PA and SA is emphasized within this Special Issue. While PA can be characterized by costly hardware, SA is defined by the use of software, such as Internet of Things (IoT), artificial neural networks (ANNs), machine learning (ML), and big data that is stored in large computing servers While SA technology is readily available through simple applications and can be user friendly, many smallholder farmers decline to invest in sophisticated sensors because of their impression that doing so is time consuming and expensive.

Participants are encouraged to submit papers for the proposed Special Issue according to the following subjects:

1. Models in precision agriculture

1.1 Evaluation and management of crop production by remote sensing

1.2 Comparison of models for crop and biomass production

1.3 Optimization models for crop production

1.4 Remote sensing for assistance and completion of data to run models

2. Methods for implementing precision agriculture

2.1 Phones and steel camera tractors, drones, ultralight aircraft, and satellites

2.2 Target group of growers for precision agriculture: Owners of small, medium, and large farms

2.3 A precision agricultural economy: Increasing the yield of economically-viable crops

3. Physics of precision agriculture

3.1 Optics

3.2 Wavelengths: RGB belt multi-spectral and hyper-spectral cameras

3.3 Resolution, pixel size, and relation to field sizes

4. Agronomy of Precision Agriculture

4.1 Orchard VS field crops

4.2 Optimizing the use of water

4.3 Optimizing the use of fertilizer

4.4 Optimization and management of crop production with the help of precision agriculture

5. Environmental impacts of precision agricultural technology on the environment

5.1 Protection of natural vegetation

5.2 Protection of agricultural crops from diseases and pests

5.3 Prevention of excess fertilization transport to the groundwater table

5.4 Public Health: Prevention of pesticide transport to populated areas

Prof. Dr. Jiftah Ben-Asher
Guest Editor

Manuscript Submission Information

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Keywords

  • Models in precision agriculture
  • Methods for implementing precision agriculture
  • Physics of precision agriculture
  • Agronomy of Precision Agriculture
  • Environmental impact of precision ag. technology on the environment.

Published Papers (16 papers)

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22 pages, 5534 KiB  
Article
Optimizing the Maize Irrigation Strategy and Yield Prediction under Future Climate Scenarios in the Yellow River Delta
by Yuyang Shan, Ge Li, Shuai Tan, Lijun Su, Yan Sun, Weiyi Mu and Quanjiu Wang
Agronomy 2023, 13(4), 960; https://doi.org/10.3390/agronomy13040960 - 23 Mar 2023
Cited by 4 | Viewed by 1438
Abstract
The contradiction between water demand and water supply in the Yellow River Delta restricts the corn yield in the region. It is of great significance to formulate reasonable irrigation strategies to alleviate regional water use and improve corn yield. Based on typical hydrological [...] Read more.
The contradiction between water demand and water supply in the Yellow River Delta restricts the corn yield in the region. It is of great significance to formulate reasonable irrigation strategies to alleviate regional water use and improve corn yield. Based on typical hydrological years (wet year, normal year, and dry year), this study used the coupling model of AquaCrop, the multi-objective genetic algorithm (NSGA-III), and TOPSIS-Entropy established using the Python language to solve the problem, with the objectives of achieving the minimum irrigation water (IW), maximum yield (Y), maximum irrigation water production rate (IWP), and maximum water use efficiency (WUE). TOPSIS-Entropy was then used to make decisions on the Pareto fronts, seeking the best irrigation decision under the multiple objectives. The results show the following: (1) The AquaCrop-OSPy model accurately simulated the maize growth process in the experimental area. The R2 values for canopy coverage (CC) in 2019, 2020, and 2021 were 0.87, 0.90, and 0.92, respectively, and the R2 values for the aboveground biomass (BIO) were 0.97, 0.96, and 0.96. (2) Compared with other irrigation treatments, the rainfall in the test area can meet the water demand of the maize growth period in wet years, and net irrigation can significantly reduce IW and increase Y, IWP, and WUE in normal and dry years. (3) Using LARS-WG (a widely employed stochastic weather generator in agricultural climate impact assessment) to generate future climate scenarios externally resulted in a higher CO2 concentration with increased production and slightly reduced IW demand. (4) Optimizing irrigation strategies is important for allowing decision makers to promote the sustainable utilization of water resources in the study region and increase maize crop yields. Full article
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12 pages, 1225 KiB  
Article
Prediction Model of Pumpkin Rootstock Seedlings Based on Temperature and Light Responses
by Zhengnan Yan, Jie Cheng, Ze Wan, Beibei Wang, Duo Lin and Yanjie Yang
Agronomy 2023, 13(2), 516; https://doi.org/10.3390/agronomy13020516 - 10 Feb 2023
Cited by 1 | Viewed by 1400
Abstract
Temperature and light are the key factors that affect the quality of pumpkin rootstock seedlings’ growth process. Responses to temperature and light are an important basis for optimizing the greenhouse environment. In order to determine the quantitative effects of temperature and light on [...] Read more.
Temperature and light are the key factors that affect the quality of pumpkin rootstock seedlings’ growth process. Responses to temperature and light are an important basis for optimizing the greenhouse environment. In order to determine the quantitative effects of temperature and light on the growth and development of pumpkin (Cucurbita moschata cv. RTWM6018) rootstock seedlings, relationships between temperature, light, and pumpkin rootstock seedlings growth were established using regression analysis. The results indicated that the daily average temperature had a significant negative correlation with the development time of pumpkin rootstock seedlings, and the shoot dry weight of pumpkin rootstock seedlings increased within a certain range of the daily light integral (DLI). We established a prediction model of pumpkin rootstock seedling quality indicators (hypocotyl length, stem diameter, shoot dry weight, root dry weight, root shoot ratio, and seedling quality index) based on thermal effectiveness and photosynthetic photon flux density (TEP). The coefficient of determinations (R2) of the hypocotyl length and seedling quality index prediction models of pumpkin rootstock seedlings, based on accumulated TEP, were 0.707 and 0.834, respectively. The hypocotyl length and seedling quality index prediction models of pumpkin rootstock seedlings, based on accumulated TEP, were y1 = 0.001 x2 − 0.180 x + 13.057 and y2 = 0.008 x0.722, respectively, which could be used for predicting the growth of pumpkin rootstock seedlings grown under different temperature and light conditions. Full article
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16 pages, 9988 KiB  
Article
Development of a Full-View-Type Grading Cup for Automated Sweet Cherry Sorters
by Xiang Han, Longlong Ren, Ziwen Shang, Baoyou Liu, Yi Liu, Yanchen Gong and Yuepeng Song
Agronomy 2023, 13(2), 500; https://doi.org/10.3390/agronomy13020500 - 9 Feb 2023
Cited by 1 | Viewed by 1357
Abstract
In order to improve the adaptability of sweet cherry sorters to sweet cherries, a full-view-type grading cup for automated sweet cherry sorters was developed based on the physical characteristics of three species of sweet cherries: Tieton, Huang Mi, and Lapins. The structure and [...] Read more.
In order to improve the adaptability of sweet cherry sorters to sweet cherries, a full-view-type grading cup for automated sweet cherry sorters was developed based on the physical characteristics of three species of sweet cherries: Tieton, Huang Mi, and Lapins. The structure and working principle of the full-view-type grading cup are described in this paper. The main factors affecting the operating stability of the grading cup were identified in this study by analyzing the mechanical properties of sweet cherries during the conveying and rotating process. According to the Box-Behnken test design method, we took the operating speed, the Young’s modulus, and the friction coefficient of the double-roller supporter as the test factors and we took the success rate as the test index. The operating parameters of the full-view-type grading cup were tested and studied, and the regression model between the test index and the test factors was established. The influence law of each factor on the test index was analyzed, and the test factors were comprehensively optimized according to the regression model. The results showed that when the operating speed was 48 sweet cherries/min; the butadiene rubber (BR) 9000, with a Young’s modulus of 0.012 GPa was selected as the material for making the double-support rollers; and the friction coefficient was designed to be 15, the success rate was 86.0%. The results of this research have provided a theoretical basis for the design and optimization of grading cups for automated sweet cherry sorters. Full article
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14 pages, 4809 KiB  
Article
Determining the Beginning of Potato Tuberization Period Using Plant Height Detected by Drone for Irrigation Purposes
by Sarah Martins, Rachid Lhissou, Karem Chokmani and Athyna Cambouris
Agronomy 2023, 13(2), 492; https://doi.org/10.3390/agronomy13020492 - 8 Feb 2023
Cited by 3 | Viewed by 1613
Abstract
Insolation and precipitation instability associated with climate change affects plant development patterns and water demand. The potato root system and soil properties lead to water vulnerability, impacting crop yield. Regarding potato physiology, plants stop growing when the root depth stabilizes, and then the [...] Read more.
Insolation and precipitation instability associated with climate change affects plant development patterns and water demand. The potato root system and soil properties lead to water vulnerability, impacting crop yield. Regarding potato physiology, plants stop growing when the root depth stabilizes, and then the tuberization period begins. Since this moment, water supply is required. Consequently, an approach based on plant physiology may enable farmers to detect the beginning of the irrigation period precisely. Remote sensing is a fast and precise method for obtaining surface information using non-invasive data collection. The database comprises root depth (RD) and plant height (H) data collected during 2019, 2020, and 2021. This research aims to develop a dynamic approach based on remote sensing and crop physiology to accurately determine the beginning of the tuberization period, called here the irrigation critical point (ICP). The results indicate a high correlation between RD and H (>0.85) which is independent of in-field soil and relief variations > 0.95). Further, plant growth rate corroborates the correlation results with decreasing patterns in time (R2 > 0.80), independent of environmental variations. In short, it was possible to determine the ICP based on the crop growth dynamics, independently of climate variations, field placement, or irrigation system. Full article
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20 pages, 19770 KiB  
Article
A Novel Segmentation Recognition Algorithm of Agaricus bisporus Based on Morphology and Iterative Marker-Controlled Watershed Transform
by Chao Chen, Shanlin Yi, Jinyi Mao, Feng Wang, Baofeng Zhang and Fuxin Du
Agronomy 2023, 13(2), 347; https://doi.org/10.3390/agronomy13020347 - 25 Jan 2023
Cited by 4 | Viewed by 1579
Abstract
Accurate recognition of Agaricus bisporus is a prerequisite for precise automatic harvesting in a factory environment. Aimed at segmenting mushrooms adhering together from the complex background, this paper proposes a watershed-based segmentation recognition algorithm for A. bisporus. First, the foreground of A. [...] Read more.
Accurate recognition of Agaricus bisporus is a prerequisite for precise automatic harvesting in a factory environment. Aimed at segmenting mushrooms adhering together from the complex background, this paper proposes a watershed-based segmentation recognition algorithm for A. bisporus. First, the foreground of A. bisporus is extracted via Otsu threshold segmentation and morphological operations. Then, a preliminary segmentation algorithm and a novel iterative marker generation method are proposed to prepare watershed markers. On this basis, a marker-controlled watershed algorithm is adopted to segment and recognize A. bisporus individuals. All the algorithms are implemented based on OpenCV (Open Source Computer Vision) libraries. Tests on images of A. bisporus collected at the cultivation bed show that the average correct recognition rate of the proposed algorithm is 95.7%, the average diameter measurement error is 1.15%, and the average coordinate deviation rate is 1.43%. The average processing time is 705.7 ms per single image, satisfying the real-time constraints based on 1 image/s. The proposed algorithm performed better than the current Circle Hough Transform (OpenCV’s implementation). It is convenient and easy to operate, providing a sound basis for subsequent research on mechanized harvesting equipment for A. bisporus. Full article
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13 pages, 1206 KiB  
Article
Estimating Chlorophyll Fluorescence Parameters of Rice (Oryza sativa L.) Based on Spectrum Transformation and a Joint Feature Extraction Algorithm
by Shuangya Wen, Nan Shi, Junwei Lu, Qianwen Gao, Huibing Yang and Zhiqiang Gao
Agronomy 2023, 13(2), 337; https://doi.org/10.3390/agronomy13020337 - 24 Jan 2023
Cited by 2 | Viewed by 1389
Abstract
The chlorophyll fluorescence parameter Fv/Fm plays a significant role in indicating the photosynthetic function of plants. The existing technical methods used to measure Fv/Fm are often inefficient and cumbersome. To realize fast and non-destructive monitoring of Fv/Fm, this study took rice under different [...] Read more.
The chlorophyll fluorescence parameter Fv/Fm plays a significant role in indicating the photosynthetic function of plants. The existing technical methods used to measure Fv/Fm are often inefficient and cumbersome. To realize fast and non-destructive monitoring of Fv/Fm, this study took rice under different fertilizer treatments and measured the hyperspectral reflectance information and Fv/Fm data of rice leaves during the whole growth period. Five spectral transformation methods were used to pre-process the spectral data. Then, spectral characteristic wavelengths were extracted by the correlation coefficient method (CC) combined with the competitive adaptative reweighted sampling (CARS) algorithm. Finally, based on the combination of characteristic wavelengths extracted from different spectral transformations, back propagation neural network (BPNN) models were constructed and evaluated. The results showed that: (1) first derivative transform (FD), multiplicative scatter correction (MSC) and standardized normal variation (SNV) methods could effectively highlight the correlation between spectral data and Fv/Fm. The most sensitive bands with high correlation coefficients were concentrated in the range of 650–850 nm, and the absolute values of the highest correlation coefficients were 0.84, 0.73, and 0.72, respectively. (2) The CC-CARS algorithm could effectively screen the characteristic wavelengths sensitive to Fv/Fm. The number of sensitive bands extracted by FD, MSC, and SNV pre-treatment methods were 14, 13, and 16 which only accounted for 2.33%, 2.16%, and 2.66% of the total spectral wavelength (the number of full spectral bands is 601), respectively. (3) The BPNN models were established based on the above sensitive wavelengths, and it was found that MSC-CC-CARS-BPNN had the highest prediction accuracy, and its testing set R2, RMSE and RPD were 0.74, 1.88% and 2.46, respectively. The results can provide technical references for hyperspectral data pre-processing and rapid and non-destructive monitoring of chlorophyll fluorescence parameters. Full article
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16 pages, 3331 KiB  
Article
Spherical Interpretation of Infiltration from Trickle Irrigation
by Jiftah Ben-Asher, Roman Volynski and Natalya Gulko
Agronomy 2022, 12(10), 2469; https://doi.org/10.3390/agronomy12102469 - 11 Oct 2022
Viewed by 1057
Abstract
The hypothesis of this paper is that infiltration into drip irrigated soils can be described by simple spherical considerations as well as two dimensional (2D) numerical modeling. The major goal was to test a very simple model based on geometry of a sphere [...] Read more.
The hypothesis of this paper is that infiltration into drip irrigated soils can be described by simple spherical considerations as well as two dimensional (2D) numerical modeling. The major goal was to test a very simple model based on geometry of a sphere formulas, and compare it with elaborated numerical solutions and field experiments. Detailed analysis of soil–water infiltration under trickle regimes is shown to be pre-requisite in the search for the optimal design of system layout. Optimality and simplicity are sought by modeling a sphere for subsurface trickle/drip (SDI) and hemisphere (DI) pattern of moisture distributions during infiltration. Numerical simulations by MATLAB software were used to describe the distribution of soil water. The data produced by this simulation were successfully compared with analytical models and numerical results of Panoche clay loam. To simulate the four discharge rates (0.5, 1, 2, 3 Lh) under DI and SDI we used the input of Panoche soil properties, i.e., hydraulic conductivity function (Kθ) and soil water retention curve (ψθ). The resulting regression equation of numerical analysis (N) vs. spherical interpretation (S) was N = 0.97 × S − 19.1; r2 = 0.98. This result exposes the novelty of the approach by showing that infiltration from a drip/trickle source can be described by simple spherical radial symmetry in addition to analytical or numerical simulations. An example of a design parameter for 3000 cm3h suggested more emitters per meter laterals for SDI than for DI (100 vs. 77 unites, respectively) due to the shorter distance between SDI emitters that are required in order to maintain wetting continuity. At a discharge of around 500 cm3h of three different soils’ SDI, positive pressure was detected near the orifice and it caused discharge reduction. This is a self-compensating property of SDI that regulates individual emitters according to the soil hydraulic properties. In conclusion SDI is associated with larger capital investment compared to DI, but it can be compensated by improving the water use efficiency due to increased productivity while reducing losses of water through evaporation, but this option should be investigated as part of specific research. Full article
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13 pages, 3486 KiB  
Article
Tender Leaf Identification for Early-Spring Green Tea Based on Semi-Supervised Learning and Image Processing
by Jie Yang and Yong Chen
Agronomy 2022, 12(8), 1958; https://doi.org/10.3390/agronomy12081958 - 19 Aug 2022
Cited by 9 | Viewed by 2348
Abstract
Tea is one of the most common beverages in the world. Automated machinery that is suitable for plucking high-quality green tea is necessary for tea plantations and the identification of tender leaves is one of the key techniques. In this paper, we proposed [...] Read more.
Tea is one of the most common beverages in the world. Automated machinery that is suitable for plucking high-quality green tea is necessary for tea plantations and the identification of tender leaves is one of the key techniques. In this paper, we proposed a method that combines semi-supervised learning and image processing to identify tender leaves. Both in two-dimensional and three-dimensional space, the three R, G, and B components of tender leaves and their backgrounds were trained and tested. The gradient-descent method and the Adam algorithm were used to optimize the objective function, respectively. The results show that the average accuracy of tender leaf identification is 92.62% and the average misjudgment rate is 18.86%. Our experiments have shown that green tea tender leaves in early spring can be identified effectively using the model based on semi-supervised learning, which has strong versatility and perfect adaptability, so as to improve the problem of deep learning requiring a large number of labeled samples. Full article
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17 pages, 3008 KiB  
Article
Performance of AquaCrop Model for Maize Growth Simulation under Different Soil Conditioners in Shandong Coastal Area, China
by Yuyang Shan, Ge Li, Lijun Su, Jihong Zhang, Quanjiu Wang, Junhu Wu, Weiyi Mu and Yan Sun
Agronomy 2022, 12(7), 1541; https://doi.org/10.3390/agronomy12071541 - 28 Jun 2022
Cited by 6 | Viewed by 2043
Abstract
Evaluating the performance of AquaCrop models under the drip irrigation of maize with soil conditioners is of great significance for improving coastal saline–alkali land crop management strategies. This study aimed to evaluate the performance of an AquaCrop model for maize growth simulation under [...] Read more.
Evaluating the performance of AquaCrop models under the drip irrigation of maize with soil conditioners is of great significance for improving coastal saline–alkali land crop management strategies. This study aimed to evaluate the performance of an AquaCrop model for maize growth simulation under different soil conditions (humic acid (HA) and sodium carboxymethyl cellulose (CMC)) and dosages and different levels of irrigation in the Shandong coastal saline–alkali area, China, and to optimize the amount of irrigation. Three years of experiments were carried out in the growing season of maize (Ludan 510) in 2019, 2020, and 2021. The dosages of HA were 5, 15, 25, and 35 g/m2, the dosages of CMC were 1, 2, 3, and 5 g/m2, and the levels of irrigation from 2019 to 2021 were all 120 mm. The model was calibrated with data from 2019, and the model was verified with data from 2020 to 2021, according to the recommended corn parameters in the AquaCrop model manual. The results showed that the model had a good simulation effect on canopy coverage, with a root-mean-square error (RMSE) of less than 15.2%, and the simulated aboveground biomass and yield were generally low. The simulated value of soil water content was generally high, with some treatments having errors of more than 15.0%. The simulation effect of irrigated maize from 2019 to 2020 was better than maize in 2021. The simulation effect of HA was better than that of CMC, while the simulation effect of a low-gradient modifier was better than that of high-gradient conditioner when compared with CMC. In conclusion, the AquaCrop model could be a viable method for predicting maize development under different soil conditioners in this area. The suitable levels of irrigation under HA and CMC treatments were 47.0–65.9 mm and 61.0–92.4 mm, respectively, according to the principle of high yield and water use efficiency. The results provided a reference for optimizing the drip irrigation of maize under the application of soil conditioners in coastal saline–alkali areas. Full article
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19 pages, 24270 KiB  
Article
Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu
by Vinson Joshua, Selwin Mich Priyadharson and Raju Kannadasan
Agronomy 2021, 11(10), 2068; https://doi.org/10.3390/agronomy11102068 - 15 Oct 2021
Cited by 26 | Viewed by 3560
Abstract
Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The [...] Read more.
Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics. Full article
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14 pages, 1973 KiB  
Article
Crop Response to Combined Availability of Soil Water and Its Salinity Level: Theory, Experiments and Validation on Golf Courses
by Jiftah Ben-Asher, Jose Beltrao, Gulom Bekmirzaev and Thomas Panagopoulos
Agronomy 2021, 11(10), 2012; https://doi.org/10.3390/agronomy11102012 - 7 Oct 2021
Cited by 6 | Viewed by 2000
Abstract
The phenomenological expression showing crop yield to be directly dependent on water deficiency, under saline conditions, has encouraged a continued focus on salinity as a viable approach to increase crop yields. This work reassesses crop response to availability of saline soil water ASW [...] Read more.
The phenomenological expression showing crop yield to be directly dependent on water deficiency, under saline conditions, has encouraged a continued focus on salinity as a viable approach to increase crop yields. This work reassesses crop response to availability of saline soil water ASW in two stages (A) Develop a simple approach suggesting that permanent wilting point (WP) increases under high saline soil water tension and relative yield of Lettuce (Lactuca sativa L., var longifolia Lam., cv. Nevada) and maize (Zea mays L., cv. Jubilee sweet) decrease. (B) Using a deterministic numerical soil water model to validate the theory on Bermuda grass of golf courses. The experimental plots were established in the North Negev, Israel (Sweet corn) and the Algarve, Portugal (Lettuce and Bermuda grass covering the golf courses). Sprinkler irrigation and line source techniques were used for water application, creating a saline gradient under a precise irrigation water distribution. Two salinity empirical models were tested (Mass and Hoffman MH and van Genuchten–Gupta vGG). Their empirical models were modified and instead of soil electrical conductivity of irrigation water (ECe) we used wilting point (WP) and RASW to follow the changes in relative yield. The validation was conducted with theoretical soil plant atmosphere water (SPAW) to predict the results on golf courses. It is concluded that an alternative S-shaped response model provides better fit to our experimental data sets. Modified MH model (Yr = Y/Ymax = a ∗ (ASW–threshold’s constant) revealed that a single dimensionless curve could be used to express yield—salinity interference when represented by varying ASW. The vGG model: vGG can represent salt tolerance of most crops, by using varying wilting point of average root zone salinity, at which the yield has declined by 50%. The abscissa of both models was based on WP rather than the standard soil electrical conductivity (ECw). The correlation between the experimental data and WP or relative available soil water (RASW) was acceptable and, therefore, their usefulness for prediction of relative yield is acceptable as well. The objectives of this study were: 1. To develop a simple model describing the effect of salinity through soil water availability on crop production; 2. To replace the standard varying soil electrical conductivity ECe used by MH and vGG models by two soil parameters (at wilting point- θwp and at field capacity θfc) in order to describe the relationship between them and relative yield. 3. Validate the new model with respect to independent salinity on Golf courses and a mathematical deterministic model. Full article
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18 pages, 6545 KiB  
Article
Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery
by Chris Cavalaris, Sofia Megoudi, Maria Maxouri, Konstantinos Anatolitis, Marios Sifakis, Efi Levizou and Aris Kyparissis
Agronomy 2021, 11(8), 1486; https://doi.org/10.3390/agronomy11081486 - 27 Jul 2021
Cited by 20 | Viewed by 2546
Abstract
In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements [...] Read more.
In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction. Full article
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14 pages, 1923 KiB  
Article
Optimum Sowing Window and Yield Forecasting for Maize in Northern and Western Bangladesh Using CERES Maize Model
by Apurba Kanti Choudhury, Md. Samim Hossain Molla, Taslima Zahan, Ranjit Sen, Jatish Chandra Biswas, Sohela Akhter, Sheikh Ishtiaque, Faruque Ahmed, Md. Maniruzaman, Md. Belal Hossain, Parimal Chandra Sarker, Eldessoky S. Dessoky, Mohamed M. Hassan and Akbar Hossain
Agronomy 2021, 11(4), 635; https://doi.org/10.3390/agronomy11040635 - 26 Mar 2021
Cited by 5 | Viewed by 3182
Abstract
Determination of the optimum sowing window not only can improve maize yield significantly but also can fit maize in the existing cropping pattern. To get the advantages of sowing maize at the optimum time, a study was designed and carried out at the [...] Read more.
Determination of the optimum sowing window not only can improve maize yield significantly but also can fit maize in the existing cropping pattern. To get the advantages of sowing maize at the optimum time, a study was designed and carried out at the research field of Bangladesh Agricultural Research Institute, Rangpur, Bangladesh during 2015–2017. Another aim of the study was to forecast the yield of maize for the northern and western regions of Bangladesh using the CERES-Maize model. The study considered 5 November, 20 November, 5 December, 20 December, and 5 January as sowing dates for maize to identify the optimum sowing window. Three hybrid maize varieties, viz., BARI Hybrid Maize-9 (BHM-9), NK-40, and Pioneer30V92 were used. The study was laid out in a split-plot design, assigning the sowing dates in the main plot and the varieties in the sub-plot. To forecast the yield, the daily weather data of 2017 were subjected to run the model along with thirty years (1986–2015) of weather data. The genetic coefficients of the tested maize varieties were obtained through calibration of the model by using the observed field data of 2015–2016 and through validation by using the data of 2016–2017. The seasonal analysis was done using the DSSAT CERES-Maize model to confirm the experimental findings for optimizing the sowing window for maize at the northern region (Rangpur) of the country and subsequently adjusted the model for the western region (Jashore). The model performances were satisfactory for crop phenology, biomass, and grain yield. The NRMSE for anthesis was 0.66% to 1.39%, 0.67% to 0.89% for maturity date, 1.78% to 3.89% for grain yield, and 1.73% to 3.17% for biomass yield. The optimum sowing window for maize at the Rangpur region was 5 November to 5 December and 5 to 20 November for the Jashore region. The CERES-Maize model was promising for yield forecasting of the tested maize varieties. It gave a realistic yield forecast at approximately 45 days prior to the harvest of all the tested varieties. The study results are expected to be useful for both the farmers and the policy planners to meet up the future maize demands. Full article
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20 pages, 2448 KiB  
Article
Proximal Sensing of Nitrogen Needs by Spring Wheat
by Shlomo Sarig, Eli Shlevin, Arkadi Zilberman, Idan Richker, Mordechay Dudai, Shlomo Nezer and Jiftah Ben-Asher
Agronomy 2021, 11(3), 437; https://doi.org/10.3390/agronomy11030437 - 27 Feb 2021
Viewed by 1823
Abstract
Canopy nitrogen (N) status relates strongly to canopy chlorophyll content and the strength of green color. Proximal photograph by RGB camera was used to select green features that has the potential to assess N content at leaf of plant as a function of [...] Read more.
Canopy nitrogen (N) status relates strongly to canopy chlorophyll content and the strength of green color. Proximal photograph by RGB camera was used to select green features that has the potential to assess N content at leaf of plant as a function of its the greenness. We proposed the development of it as a tool for sensing nitrogen content in spring wheat (Triticum aestivum). Image processing algorithm was programed calibrated and validated wheat %N%N. Nitrogen uptake =%N × canopy dry matter was harvested and calculated using simulated dry matter by DSSAT model. The data replicated laboratory measurements. A linear Lab vs Camera model displayed a unit slope with r2 = 0.93. Increase of dry matter was successfully surrogated by days after emergence and used as abscissa for inverse logistic model of critical nitrogen level. It decreased gradually from about 6% to 2% as days after emergence increased from 0 to 110 days. Maximum N uptake calculated from photo and laboratory was 324 Kg ha−1 and 318 Kg ha−1 respectively suggesting insignificant difference. Physiological N-use efficiency (i.e., canopy weight/N weight) was 52 and 78 kg canopy dry weight per 1 kg N for early and late-ripening cultivars, respectively. The determination of N application based on the smartphone photograph proved to be useful by saving on time and expenses for growers who have access to smartphones and can use them for N application and management. Full article
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Review

Jump to: Research, Other

22 pages, 4319 KiB  
Review
Mobile Computing for Pest and Disease Management Using Spectral Signature Analysis: A Review
by Nik Norasma Che’Ya, Nur Adibah Mohidem, Nor Athirah Roslin, Mohammadmehdi Saberioon, Mohammad Zakri Tarmidi, Jasmin Arif Shah, Wan Fazilah Fazlil Ilahi and Norsida Man
Agronomy 2022, 12(4), 967; https://doi.org/10.3390/agronomy12040967 - 16 Apr 2022
Cited by 14 | Viewed by 4933
Abstract
The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes; one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and [...] Read more.
The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes; one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and precautions to prevent further infection from occurring. Furthermore, farmers can communicate with agricultural authorities to manage their farm from home, and efficiently obtain information such as the spectral signature of crops. Therefore, the spectral signature can be used as a reference to detect pests and diseases with a hyperspectral sensor more efficiently than the conventional method, which takes more time to monitor the entire crop field. This review aims to show the current and future trends of mobile computing based on spectral signature analysis for pest and disease management. In this review, the use of mobile applications for pest and disease monitoring is evaluated based on image processing, the systems developed for pest and disease extraction, and the structure of steps outlined in developing a mobile application. Moreover, a comprehensive literature review on the utilisation of spectral signature analysis for pest and disease management is discussed. The spectral reflectance used in monitoring plant health and image processing for pest and disease diagnosis is mentioned. The review also elaborates on the integration of a spectral signature library within mobile application devices to obtain information about pests and disease in crop fields by extracting information from hyperspectral datasets. This review demonstrates the necessary scientific knowledge for visualising the spectral signature of pests and diseases using a mobile application, allowing this technology to be used in real-world agricultural settings. Full article
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Other

Jump to: Research, Review

13 pages, 449 KiB  
Brief Report
Identification of the Spectral Patterns of Cultivated Plants and Weeds: Hyperspectral Vegetation Indices
by Roman Danilov, Oksana Kremneva and Alexey Pachkin
Agronomy 2023, 13(3), 859; https://doi.org/10.3390/agronomy13030859 - 15 Mar 2023
Cited by 2 | Viewed by 1237
Abstract
The accurate recognition of weeds on crops supports the spot application of herbicides, the high economic effect and reduction of pesticide pressure on agrocenoses. We consider the approach based on the quantitative spectral characteristics of plant objects to be the most appropriate for [...] Read more.
The accurate recognition of weeds on crops supports the spot application of herbicides, the high economic effect and reduction of pesticide pressure on agrocenoses. We consider the approach based on the quantitative spectral characteristics of plant objects to be the most appropriate for the development of methods for the spot application of herbicides. We made test plots with different species composition of cultivated and weed plants on the experimental fields of the scientific crop rotation of the Federal Research Center of Biological Plant Protection. These plants form the basis of the agrocenoses of Krasnodar Krai. Our primary subjects are sunflower crops (Helianthus annuus L.), corn (Zea mais L.) and soybean (Glycine max (L.)). Besides the test plots, pure and mixed backgrounds of weeds were identified, represented by the following species: ragweed (Ambrosia artemisiifolia L.), California-bur (Xanthium strumarium L.), red-root amaranth (Amaranthus retroflexus L.), white marrow (C. album L.) and field milk thistle (Sonchus arvensis L.). We used the Ocean Optics Maya 2000-Pro automated spectrometer to conduct high-precision ground-based spectrometric measurements of selected plants. We calculated the values of 15 generally accepted spectral index dependencies based on data processing from ground hyperspectral measurements of cultivated and weed plants. They aided in evaluating certain vegetation parameters. Factor analysis determined the relationship structure of variable values of hyperspectral vegetation indices into individual factor patterns. The analysis of variance assessed the information content of the indicators of index values within the limits of the selected factors. We concluded that most of the plant objects under consideration are characterized by the homogeneity of signs according to the values of the index indicators that make up the selected factors. However, in most of the cases, it is possible to identify different plant backgrounds, both by the values of individual vegetation indices and by generalized factorial coefficients. Our research results are important for the validation of remote aerospace observations using multispectral and hyperspectral instruments. Full article
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