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Sensors and Associated Artificial Intelligence in Agricultural Applications for Specialty Crops

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 52410

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Special Issue Editors

Dr. Marcus Nagle

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Guest Editor

Agricultural Research and Development Program, Central State University, Wilberforce, OH 45384, USA
Interests: horticulture; postharvest; food science; crop physiology; agricultural engineering; nondestructive sensors

Dr. Deng Cao

E-Mail Website
Guest Editor

Mathematics & Computer Science Faculty, Central State University, Wilberforce, OH 45384, USA
Interests: computer vision; advanced biometrics; pattern recognition and machine learning

Dr. Cadance Lowell

E-Mail Website
Guest Editor

Department of Agricultural Sciences, Central State University, Wilberforce, OH 45384, USA
Interests: mechantronics; integrated systems; directed energy; weed science; sustainable agriculture

Special Issue Information

Dear Colleagues,

A critical aspect of precision agriculture is not only the development and application of sensors for grain crop production, but also for the smaller-scale yet high-value fruit and vegetable commodities. The past two decades have seen an upsurge in research, with many marketable developments as a result. This field of study continues to develop, especially with the discovery of new sensing principles and the reduced cost of existing technologies which make them practical to apply in agriculture. Smart technology and the Internet-of-Things, which have recently emerged, are assured to find their place in agricultural settings as well, and science and industry are already active with making that a reality. In this Special Issue, we welcome contributions that use established and novel sensing technologies and methods to monitor and predict growth, disease, maturation, ripening, mechanical damage, and general quality of specialty crops during production, harvest, and postharvest phases. The research presented will highlight the current work being done by various research groups around the world to make fruit and vegetable production truly 21st-century.

Sensor topics may cover but are not limited to: optical, acoustics, mechanical, electrochemical, nanomaterials, programming, modeling, calibration, and artificial intelligence. Specialty crops can also include herbs, medicinal plants, ornamentals, cut flowers, etc.

Dr. Marcus Nagle
Dr. Deng Cao
Dr. Cadance Lowell
Guest Editors

Manuscript Submission Information

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Keywords

Agricultural sensors
Specialty crops
Fruits and vegetables
Precision agriculture
Artificial intelligence
Food engineering

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Published Papers (6 papers)

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Research

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30 pages, 12894 KiB

Open AccessArticle

Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality

by Silke Hemming, Feije de Zwart, Anne Elings, Anna Petropoulou and Isabella Righini

Sensors 2020, 20(22), 6430; https://doi.org/10.3390/s20226430 - 11 Nov 2020

Cited by 65 | Viewed by 20139

Abstract

Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second “Autonomous Greenhouse Challenge”. In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production. Full article

(This article belongs to the Special Issue Sensors and Associated Artificial Intelligence in Agricultural Applications for Specialty Crops)

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20 pages, 2967 KiB

Open AccessArticle

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

by Vu Ngoc Tuan, Abdul Mateen Khattak, Hui Zhu, Wanlin Gao and Minjuan Wang

Sensors 2020, 20(18), 5314; https://doi.org/10.3390/s20185314 - 17 Sep 2020

Cited by 7 | Viewed by 4903

Abstract

Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic strength make them difficult to apply in a hydroponic system. To minimize these deficiencies, we combined the multivariate standard addition (MSAM) sampling technique with the deep kernel learning (DKL) model for a six ISEs array to increase the prediction accuracy and precision of eight ions, including

N O_{3}^{-}

N H_{4}^{+}

K^{+}

C a^{2 +}

N a^{+}

C l^{-}

H_{2} P O_{4}^{-}

, and

M g^{2 +}

. The enhanced data feature based on feature enrichment (FE) of the MSAM technique provided more useful information to DKL for improving the prediction reliability of the available ISE ions and enhanced the detection of unavailable ISE ions (phosphate and magnesium). The results showed that the combined MSAM–feature enrichment (FE)–DKL sensing structure for validating ten real hydroponic samples achieved low root mean square errors (RMSE) of 63.8, 8.3, 29.2, 18.5, 11.8, and 8.8

mg \cdot L^{- 1}

with below 8% coefficients of variation (CVs) for predicting nitrate, ammonium, potassium, calcium, sodium, and chloride, respectively. Moreover, the prediction of phosphate and magnesium in the ranges of 5–275 mg·L⁻¹ and 10–80

mg \cdot L^{- 1}

had RMSEs of 29.6 and 8.7

mg \cdot L^{- 1}

respectively. The results prove that the proposed approach can be applied successfully to improve the accuracy and feasibility of ISEs in a closed hydroponic system. Full article

(This article belongs to the Special Issue Sensors and Associated Artificial Intelligence in Agricultural Applications for Specialty Crops)

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22 pages, 4832 KiB

Open AccessArticle

In Field Detection of Downy Mildew Symptoms with Proximal Colour Imaging

by Florent Abdelghafour, Barna Keresztes, Christian Germain and Jean-Pierre Da Costa

Sensors 2020, 20(16), 4380; https://doi.org/10.3390/s20164380 - 5 Aug 2020

Cited by 16 | Viewed by 3610

Abstract

This paper proposes to study the potentialities of on-board colour imaging for the in-field detection of a textbook case disease: the grapevine downy mildew. It introduces an algorithmic strategy for the detection of various forms of foliar symptoms on proximal high-resolution images. The proposed strategy is based on structure–colour representations and probabilistic models of grapevine tissues. It operates in three steps: (i) Formulating descriptors to extract the characteristic and discriminating properties of each class. They combine the Local Structure Tensors (LST) with colorimetric statistics calculated in pixel’s neighbourhood. (ii) Modelling the statistical distributions of these descriptors in each class. To account for the specific nature of LSTs, the descriptors are mapped in the Log-Euclidean space. In this space, the classes of interest can be modelled with mixtures of multivariate Gaussian distributions. (iii) Assigning each pixel to one of the classes according to its suitability to their models. The decision method is based on a “seed growth segmentation” process. This step exploits statistical criteria derived from the probabilistic model. The resulting processing chain reliably detects downy mildew symptoms and estimates the area of the affected tissues. A leave-one-out cross-validation is conducted on a dataset constituted of a hundred independent images of grapevines affected only by downy mildew and/or abiotic stresses. The proposed method achieves an extensive and accurate recovery of foliar symptoms, with on average, a 83% pixel-wise precision and a 76% pixel-wise recall. Full article

(This article belongs to the Special Issue Sensors and Associated Artificial Intelligence in Agricultural Applications for Specialty Crops)

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14 pages, 5443 KiB

Open AccessArticle

Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network

by Qian Yan, Baohua Yang, Wenyan Wang, Bing Wang, Peng Chen and Jun Zhang

Sensors 2020, 20(12), 3535; https://doi.org/10.3390/s20123535 - 22 Jun 2020

Cited by 106 | Viewed by 8355

Abstract

Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed. Full article

(This article belongs to the Special Issue Sensors and Associated Artificial Intelligence in Agricultural Applications for Specialty Crops)

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16 pages, 6251 KiB

Open AccessArticle

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

by Dae-Hyun Jung, Hak-Jin Kim, Joon Yong Kim, Taek Sung Lee and Soo Hyun Park

Sensors 2020, 20(6), 1756; https://doi.org/10.3390/s20061756 - 22 Mar 2020

Cited by 30 | Viewed by 5797

Abstract

Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R² of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic and the developed control logic; a comparison of the results showed RMSEs of 3.01 °C and 2.45 °C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system. Full article

(This article belongs to the Special Issue Sensors and Associated Artificial Intelligence in Agricultural Applications for Specialty Crops)

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Review

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28 pages, 1231 KiB

Open AccessReview

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

by Haoyu Niu, Derek Hollenbeck, Tiebiao Zhao, Dong Wang and YangQuan Chen

Sensors 2020, 20(22), 6427; https://doi.org/10.3390/s20226427 - 10 Nov 2020

Cited by 45 | Viewed by 8391

Abstract

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks. Full article

(This article belongs to the Special Issue Sensors and Associated Artificial Intelligence in Agricultural Applications for Specialty Crops)

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