Smart Horticulture: Latest Advances and Prospects

A special issue of Horticulturae (ISSN 2311-7524).

Deadline for manuscript submissions: 5 September 2024 | Viewed by 15155

Special Issue Editors


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Guest Editor
Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, 33100 Udine, Italy
Interests: remote sensing; precision agriculture; imaging; digital viticulture

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Guest Editor
Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020 Legnaro, Italy
Interests: viticulture; precision and digital agriculture; remote sensing; satellite; gis; object detection; image analysis; viticulture mechanization; agricultural robotics; site-specific management
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Guest Editor
Department of Viticulture and Enology, California State University, Fresno, CA 93740, USA
Interests: precision viticulture; remote sensing; satellite images; UAV

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Guest Editor
Department of Agronomy, Food, Natural Resources, Animals and Environment—DAFNAE, University of Padua, 35020 Legnaro, Italy
Interests: precision weed control; invasive weed species; seed germination; weed emergence; innovative solutions for weed control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart technologies are arousing increasing interest in the horticultural sector as they enable real-time monitoring and fast decision making. Horticulture could benefit from the progress in sensing technologies, robotics and artificial intelligence by implementing new labour- and cost-saving approaches. Such approaches may reduce agricultural inputs, thus increasing farms’ sustainability and resilience. However, the feasibility of a massive adoption of smart horticulture technologies is low unless some practical issues can be overcome. Farm size, cost-benefits analysis and farmers’ acceptance constitute some aspects that research should address.

This Special Issue aims to provide a global perspective of the opportunities and constraints of smart horticulture. Papers addressing the latest advances in smart technologies in the horticultural sector are welcomed. Moreover, research focusing on the prospects for technology adoption will also be considered.

This Special Issue will accept original research papers, methods, reviews from a wide variety of perspectives.

Dr. Alessia Cogato
Dr. Marco Sozzi
Dr. Eve Laroche-Pinel
Dr. Nebojša Nikolić
Guest Editors

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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

  • precision agriculture
  • sensors
  • machine learning
  • robotics
  • digital agriculture

Published Papers (10 papers)

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Research

13 pages, 2642 KiB  
Article
Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy
by Reyhaneh Gorji, Jan Skvaril and Monica Odlare
Horticulturae 2024, 10(4), 336; https://doi.org/10.3390/horticulturae10040336 - 28 Mar 2024
Viewed by 540
Abstract
Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the [...] Read more.
Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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14 pages, 5663 KiB  
Article
A Machine Learning-Assisted Three-Dimensional Image Analysis for Weight Estimation of Radish
by Yuto Kamiwaki and Shinji Fukuda
Horticulturae 2024, 10(2), 142; https://doi.org/10.3390/horticulturae10020142 - 31 Jan 2024
Viewed by 762
Abstract
The quality of radish roots depends largely on its cultivar, production environment, and postharvest management along the supply chain. Quality monitoring of fresh products is of utmost importance during the postharvest period. The purpose of this study is to nondestructively estimate the weight [...] Read more.
The quality of radish roots depends largely on its cultivar, production environment, and postharvest management along the supply chain. Quality monitoring of fresh products is of utmost importance during the postharvest period. The purpose of this study is to nondestructively estimate the weight of a radish using random forests based on color and shape information obtained from images, as well as volumetric information obtained by analyzing a point cloud obtained by combining multiple forms of shape information. The explanatory variables were color and shape information obtained through an image analysis of still images of radishes captured in a constructed photographic environment. The volume information was calculated from the bounding box and convex hull applied to the point cloud by combining the shape information obtained from the image analysis. We then applied random forests to relate the radish weight to the explanatory variables. The experimental results showed that the models using color, shape, or volume information all exhibited good performance with a Pearson’s correlation coefficient (COR) ≥ 0.80, suggesting the potential of nondestructive monitoring of radish weight based on color, shape, and volume information. Specifically, the model using volume information showed very high performance, with a COR of 0.95 or higher. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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20 pages, 1499 KiB  
Article
Insights from a Patent Portfolio Analysis on Sensor Technologies for Measuring Fruit Properties
by Žarko Kevrešan, Jasna Mastilović, Dragan Kukolj, Dragana Ubiparip Samek, Renata Kovač, Marina Đerić, Aleksandra Bajić, Gordana Ostojić and Stevan Stankovski
Horticulturae 2024, 10(1), 30; https://doi.org/10.3390/horticulturae10010030 - 28 Dec 2023
Viewed by 873
Abstract
A patent portfolio focusing on sensors for the measurement of fruit properties was generated and analyzed with the aim of contributing to a better understanding of the trends in the development and application of sensors intended for measuring fruit properties and their changes. [...] Read more.
A patent portfolio focusing on sensors for the measurement of fruit properties was generated and analyzed with the aim of contributing to a better understanding of the trends in the development and application of sensors intended for measuring fruit properties and their changes. A patent portfolio of 189 patents, utility models and patent applications was formed. Three groups of patents were identified: (i) sensor-based measurement of individual parameters, (ii) multisensor solutions for the simultaneous monitoring of multiple relevant aspects and (iii) solutions integrating sensor-derived data with artificial intelligence tools and techniques. The analysis of the patent portfolio pointed out the main driving forces of technology strengthening in the field of fruit property measurement. The development of sensing technologies enables the real-time, rapid and cost-effective determination of ever-increasing and more sophisticated sets of fruit properties and environmental conditions. Solutions integrating different sensing technologies into multisensor systems for monitoring fruit quality, ripening or freshness as holistic concepts opens avenues for the introduction of a new approach to fresh produce management. Increasing numbers of solutions introducing the application of artificial intelligence tools such as computer vision, machine learning and deep learning into the fresh produce supply chain contribute to the possibilities of substituting human decision-making at points of relevance for fresh produce management with optimal evidence-based solutions. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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16 pages, 4618 KiB  
Article
Three-Dimensional Imaging in Agriculture: Challenges and Advancements in the Phenotyping of Japanese Quinces in Latvia
by Edīte Kaufmane, Edgars Edelmers, Kaspars Sudars, Ivars Namatēvs, Arturs Nikulins, Sarmīte Strautiņa, Ieva Kalniņa and Astile Peter
Horticulturae 2023, 9(12), 1347; https://doi.org/10.3390/horticulturae9121347 - 17 Dec 2023
Viewed by 1000
Abstract
This study presents an innovative approach to fruit measurement using 3D imaging, focusing on Japanese quince (Chaenomeles japonica) cultivated in Latvia. The research consisted of two phases: manual measurements of fruit parameters (length and width) using a calliper and 3D imaging [...] Read more.
This study presents an innovative approach to fruit measurement using 3D imaging, focusing on Japanese quince (Chaenomeles japonica) cultivated in Latvia. The research consisted of two phases: manual measurements of fruit parameters (length and width) using a calliper and 3D imaging using an algorithm based on k-nearest neighbors (k-NN), the ingeniously designed “Imaginary Square” method, and object projection analysis. Our results revealed discrepancies between manual measurements and 3D imaging data, highlighting challenges in the precision and accuracy of 3D imaging techniques. The study identified two primary constraints: variability in fruit positioning on the scanning platform and difficulties in distinguishing individual fruits in close proximity. These limitations underscore the need for improved algorithmic capabilities to handle diverse spatial orientations and proximities. Our findings emphasize the importance of refining 3D scanning techniques for better reliability and accuracy in agricultural applications. Enhancements in image processing, depth perception algorithms, and machine learning models are crucial for effective implementation in diverse agricultural scenarios. This research not only contributes to the scientific understanding of 3D imaging in horticulture but also underscores its potential and limitations in advancing sustainable and productive farming practices. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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17 pages, 2688 KiB  
Article
Exploring Efficient Methods for Using Multiple Spectral Reflectance Indices to Establish a Prediction Model for Early Drought Stress Detection in Greenhouse Tomato
by Shih-Lun Fang, Yu-Jung Cheng, Yuan-Kai Tu, Min-Hwi Yao and Bo-Jein Kuo
Horticulturae 2023, 9(12), 1317; https://doi.org/10.3390/horticulturae9121317 - 07 Dec 2023
Viewed by 902
Abstract
Early detection of drought stress in greenhouse tomato (Solanum lycopersicum) is an important issue. Real-time and nondestructive assessment of plant water status is possible by spectroscopy. However, spectral data often suffer from the problems of collinearity, class imbalance, and class overlap, [...] Read more.
Early detection of drought stress in greenhouse tomato (Solanum lycopersicum) is an important issue. Real-time and nondestructive assessment of plant water status is possible by spectroscopy. However, spectral data often suffer from the problems of collinearity, class imbalance, and class overlap, which require some effective strategies to overcome. This study used a spectroscopic dataset on the tomato (cv. ‘Rosada’) vegetative stage and calculated ten spectral reflectance indices (SRIs) to develop an early drought detection model for greenhouse tomatoes. In addition, this study applied the random forest (RF) algorithm and two resampling techniques to explore efficient methods for analyzing multiple SRI data. It was found that the use of the RF algorithm to build a prediction model could overcome collinearity. Moreover, the synthetic minority oversampling technique could improve the model performance when the data were imbalanced. For class overlap in high-dimensional data, this study suggested that two to three important predictors can be screened out, and it then used a scatter plot to decide whether the class overlap should be addressed. Finally, this study proposed an RF model for detecting early drought stress based on three SRIs, namely, RNDVI, SPRI, and SR2, which only needs six spectral wavebands (i.e., 510, 560, 680, 705, 750, and 900 nm) to achieve more than 85% accuracy. This model can be a useful and cost-effective tool for precise irrigation in greenhouse tomato production, and its sensor prototype can be developed and tested in different situations in the future. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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15 pages, 3165 KiB  
Article
Indoor Plant Soil-Plant Analysis Development (SPAD) Prediction Based on Multispectral Indices and Soil Electroconductivity: A Deep Learning Approach
by Dorijan Radočaj, Irena Rapčan and Mladen Jurišić
Horticulturae 2023, 9(12), 1290; https://doi.org/10.3390/horticulturae9121290 - 30 Nov 2023
Viewed by 1057
Abstract
Leaf Soil-Plant Analysis Development (SPAD) prediction is a crucial measure of plant health and is essential for optimizing indoor plant management. The deep learning methods offer advanced tools for precise evaluations but their adaptation to the heterogeneous indoor plant ecosystem presents distinct challenges. [...] Read more.
Leaf Soil-Plant Analysis Development (SPAD) prediction is a crucial measure of plant health and is essential for optimizing indoor plant management. The deep learning methods offer advanced tools for precise evaluations but their adaptation to the heterogeneous indoor plant ecosystem presents distinct challenges. This study assesses how accurately deep neural network (DNN) predicts SPAD values in leaves on indoor plants when compared to well-established machine learning techniques, including Random Forest (RF) and Extreme Gradient Boosting (XGB). The covariates for prediction were based on low-cost multispectral and soil electro-conductivity (EC) sensors, enabling a non-destructive sensing approach. The study also strongly emphasized multicollinearity analysis quantified by the Variance Inflation Factor (VIF) and two independent indices, as well as its effect on prediction accuracy using deep and machine learning methods. DNN resulted in higher accuracy to RF and XGB, also performing better using filtered data after multicollinearity analysis based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) (R2 = 0.589, RMSE = 11.68, MAE = 9.52) in comparison to using all input covariates (R2 = 0.476, RMSE = 12.90, MAE = 10.94). Overall, DNN was proven as a more accurate prediction method than the conventional machine learning approach for the prediction of leaf SPAD values in indoor plants, despite using heterogenous plant types and input covariates. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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15 pages, 1522 KiB  
Article
Usage of Machine Learning Algorithms for Establishing an Effective Protocol for the In Vitro Micropropagation Ability of Black Chokeberry (Aronia melanocarpa (Michx.) Elliott)
by Fatih Demirel, Remzi Uğur, Gheorghe Cristian Popescu, Serap Demirel and Monica Popescu
Horticulturae 2023, 9(10), 1112; https://doi.org/10.3390/horticulturae9101112 - 09 Oct 2023
Cited by 4 | Viewed by 999
Abstract
The primary objective of this research was to ascertain the optimal circumstances for the successful growth of black chokeberry (Aronia melanocarpa (Michx.) Elliott) using tissue culture techniques. Additionally, the study aimed to explore the potential use of machine learning algorithms in this [...] Read more.
The primary objective of this research was to ascertain the optimal circumstances for the successful growth of black chokeberry (Aronia melanocarpa (Michx.) Elliott) using tissue culture techniques. Additionally, the study aimed to explore the potential use of machine learning algorithms in this context. The present research investigated a range of in vitro parameters such as total number of roots (TNR), longest root length (LRL), average root length (ARL), number of main roots (NMR), number of siblings (NS), shoot length (SL), shoot diameter (SD), leaf width (LW), and leaf length (LL) for Aronia explants cultivated in different media (Murashige and Skoog (MS) and woody plant medium (WPM)) with different concentrations (0, 0.5, 1, 1.5, and 2 mg L−1) of indole-3-butyric acid (IBA). The study showed that IBA hormone levels may affect WPM properties, affecting the LRL and ARL variables. Aronia explant media treated with 2 mg L−1 IBA had the greatest TNR, NMR, NS, SL, and SD values; 31.67 pieces, 2.37 pieces, 5.25 pieces, 66.60 mm, and 2.59 mm, in that order. However, Aronia explants treated with 1 mg L−1 IBA had the highest LW (9.10 mm) and LL (14.58 mm) values. Finally, Aronia explants containing 0.5 mg L−1 IBA had the greatest LRL (89.10 mm) and ARL (57.57 mm) values. In general, the results observed (TNR, LRL, ARL, NMR, NS, SL, SD, LW, and LL) indicate that Aronia explants exhibit superior growth and development in WPM (25.68 pieces, 68.10 mm, 51.64 mm, 2.17 pieces, 4.33 pieces, 57.95 mm, 2.49 mm, 8.08 mm, and 14.26 mm, respectively) as opposed to MS medium (20.27 pieces, 59.92 mm, 47.25 mm, 1.83 pieces, 3.57 pieces, 49.34 mm, 2.13 mm, 6.99 mm, and 12.21 mm, respectively). In the context of the in vitro culturing of Aronia explants utilizing MS medium and WPM, an analysis of machine learning models revealed that the XGBoost and SVM models perform better than the RF, KNN, and GP models when it comes to making predictions about those variables. In particular, the XGBoost model stood out due to the fact that it had the greatest R-squared value, and showed higher predictive ability in terms of properly forecasting values in comparison to actual outcomes. The findings of a linear regression (LR) analysis were used in order to conduct an efficacy study of the XGBoost model. The LR results especially confirmed the findings for the SD, NS, and NMR variables, whose R-squared values were more than 0.7. This demonstrates the extraordinary accuracy that XGboost has in predicting these particular variables. As a consequence of this, it is anticipated that it will be beneficial to make use of the XGboost model in the dosage optimization and estimation of in vitro parameters in micropropagation studies of the Aronia plant for further scientific investigation. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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22 pages, 7757 KiB  
Article
Development and Testing of an IoT Spectroscopic Nutrient Monitoring System for Use in Micro Indoor Smart Hydroponics
by Joseph D Stevens, David Murray, Dean Diepeveen and Danny Toohey
Horticulturae 2023, 9(2), 185; https://doi.org/10.3390/horticulturae9020185 - 01 Feb 2023
Cited by 10 | Viewed by 2878
Abstract
Nutrient monitoring in Micro Indoor Smart Hydroponics (MISH) relies on measuring electrical conductivity or total dissolved solids to determine the amount of nutrients in a hydroponic solution. Neither method can distinguish concentrations of individual nutrients. This study presents the development and testing of [...] Read more.
Nutrient monitoring in Micro Indoor Smart Hydroponics (MISH) relies on measuring electrical conductivity or total dissolved solids to determine the amount of nutrients in a hydroponic solution. Neither method can distinguish concentrations of individual nutrients. This study presents the development and testing of a novel spectroscopic sensor system to monitor nitrogen changes in nutrient solutions for MISH systems. The design phase determined that using an inexpensive AS7265x Internet of Thing (IoT) sensor in a transflective spectroscopic application could effectively detect small fluctuations in nitrogen concentraation. Next, a novel transflective sensor apparatus was designed and constructed for use in a MISH system experiment, growing lettuce over 30 days. Two solution tanks of different sizes, 80 L and 40 L, were used in the deployment of the system. Samples from each tank were analyzed for nitrogen concentration in a laboratory, and multilinear regression was used to predict the nitrogen concentrations using the AS7265x 18 spectral channels recorded in the sensor system. Significant results were found for both tanks with an R2 of 0.904 and 0.911 for the 80 and 40 L tanks, respectively. However, while the use of all wavelengths produced an accurate model, none of the individual wavelengths were indicative on their own. These findings indicate that the novel system presented in this study successfully and accurately monitors changes in nitrogen concentrations for MISH systems, using low cost IoT sensors. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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19 pages, 1423 KiB  
Article
Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress
by Mohamed A. Sharaf-Eldin, Salah Elsayed, Adel H. Elmetwalli, Zaher Mundher Yaseen, Farahat S. Moghanm, Mohssen Elbagory, Sahar El-Nahrawy, Alaa El-Dein Omara, Andrew N. Tyler and Osama Elsherbiny
Horticulturae 2023, 9(1), 79; https://doi.org/10.3390/horticulturae9010079 - 07 Jan 2023
Cited by 1 | Viewed by 1892
Abstract
Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the [...] Read more.
Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing can be a reliable technique for offering a rapid and precise instrument and localized management tool. This study tested the ability of proximal hyperspectral remotely sensed data to predict squash traits in two successive seasons (spring and fall) with varying moisture and potassium rates. Spectral data were collected from drip-irrigated squash that had been treated to varied rates of irrigation and potassium fertilization over both investigated seasons. To forecast potassium-use efficiency (KUE), chlorophyll meter (Chlm), water-use efficiency (WUE), and seed yield (SY) of squash, different commonly used and newly-introduced spectral index values for three bands (3D-SRIs), as well as a Decision Tree (DT) model, were evaluated. The results revealed that the newly constructed three-band SRIs based on the wavelengths of the visible (VIS), near-infrared (NIR), and red-edge regions were sensitive enough to measure the four tested parameters of squash in this study. For instance, NDI558,646,708 presented the highest R2 of 0.75 for KUE, NDI744,746,738 presented the highest R2 of 0.65 for Chlm, and NDI670,628,392 presented the highest R2 of 0.64 for SY of squash. The results further demonstrated that the principal component analysis (PCA) demonstrated the ability to distinguish moisture stress from potassium deficiency stress at the flowering stage onwards. Combining 3D-SRIs, DT-based bands (DT-b), and the aggregate of all spectral characteristics (ASF) with DT models would be an effective strategy for estimating four observed parameters with appropriate accuracy. For example, the model’s approximately 30 spectral characteristics were extremely important for predicting KUE. Its outputs with R2 were, for the training and validation datasets, 0.967 (RMSE = 0.175) and 0.818 (RMSE = 0.284), respectively. For measuring Chlm, the DT-DT-b-20 model demonstrated the best. In the training and validation datasets, the R2 value was 0.993 (RMSE = 0.522) and 0.692 (RMSE = 2.321), respectively. The overall outcomes showed that proximal-reflectance-sensing-based 3D-SRIs and DT models based on 3D-SRIs, DT-b, and ASF could be used to evaluate the four tested parameters of squash under different levels of irrigation regimes and potassium fertilizer. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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16 pages, 91228 KiB  
Article
Obtaining and Validating High-Density Coffee Yield Data
by Maurício Martello, José Paulo Molin and Helizani Couto Bazame
Horticulturae 2022, 8(5), 421; https://doi.org/10.3390/horticulturae8050421 - 09 May 2022
Cited by 6 | Viewed by 2534
Abstract
Coffee producers are ever more interested in understanding the dynamics of coffee’s spatial and temporal variability. However, it is necessary to obtain high-density yield data for decision-making. The objective of this study is to evaluate the quality of yield data obtained through a [...] Read more.
Coffee producers are ever more interested in understanding the dynamics of coffee’s spatial and temporal variability. However, it is necessary to obtain high-density yield data for decision-making. The objective of this study is to evaluate the quality of yield data obtained through a yield monitor onboard a coffee harvester, as well as to evaluate the potential of the data collected over three harvests. The yield monitor validation data showed a high correlation (above R2 0.968) when compared with the data obtained by a wagon instrumented with load cells. It was also possible to obtain yield maps for three consecutive seasons, allowing the identification of their internal variability, as well as classifying regions that show alternating yield patterns between years as the expression of the biennial yield behavior manifested inside and along the field, in addition to the spatial variability. This result indicates that, in addition to knowing the spatial yield variability, the biennial variance information must also be considered in the strategies for site-specific management. Regions that presented high yield variance should be alternated according to the productive year (high and low yield) and not only in consideration of their yield variability as on the regions with more stable yield behavior over time. The use of yield data can help the producer make more assertive decisions for crop and farm management. Full article
(This article belongs to the Special Issue Smart Horticulture: Latest Advances and Prospects)
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