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Emerging Technologies and Tools for Next-Generation Plant Growth Management

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A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Crop Physiology and Crop Production".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 15136

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Special Issue Editors

Dr. Bikram Pratap Banerjee

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Guest Editor

Agriculture Victoria, Horsham, Melbourne, VIC 3401, Australia
Interests: plant phenotyping; image matching; leaf area index; hyperspectral data; precision agriculture

Prof. Dr. Dong Wang

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Guest Editor

College of Agronomy, Northwest A&F University, Yangling 712100, China
Interests: plant physiology; plant nutrition; crop sciences; high-yield cultivation; wheat; nitrogen; water use efficiency
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Special Issue Information

Dear Colleagues,

The increasing global population is putting increased demand on food supply. Moreover, deteriorating soil quality, drought, flooding, rising temperatures, and novel plant diseases negatively impact yields worldwide. One approach to food security is plant health monitoring and rapid detection of disease, nutrient deficiencies, or drought. The advancement in sensing technologies enables acquiring data efficiently with unprecedented resolutions for timely non-destructive monitoring. Emerging technologies such as unattended aerial vehicles (UAVs) coupled with sensor systems are increasingly used to capture crop growth. Together with traditional aerial and satellite-based systems, these systems provide monitoring data at various spatial and temporal scales. More recently, many newly developed sensors and data acquisition technologies have been developed to further support crop growth monitoring and yield prediction. Combining new data-processing algorithms (e.g., machine learning and big data architecture) and high-performance computers is expected to generate a better agricultural outcome.

In this Special Issue, we aim to disseminate the latest research findings in exploiting emerging technologies for smart agriculture, where its adoption can significantly contribute to decision-making and practical management interventions. It includes, but is not limited to, crop classification; monitoring diseases, pests, weeds, water stress, and nutrient deficiencies; crop modelling; predicting yield potential and its variability; and execution of management interventions.

Dr. Bikram Pratap Banerjee
Prof. Dr. Dong Wang
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. Plants is an international peer-reviewed open access semimonthly 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 2700 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

smart agriculture/farming
multi-source information fusion
crop biophysical and biochemical parameter (e.g., LAI/fAPAR, leaf chlorophyll, and leaf nitrogen) retrieval
crop stress monitoring (e.g., nutrients, pests, diseases, drought, and heat stress)
crop biomass and yield prediction
in-season crop type classification
sustainable agricultural practices
remote sensing
satellite/UAV
machine learning

Published Papers (7 papers)

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Research

15 pages, 7533 KiB

Open AccessArticle

Evidence of Xylella fastidiosa Infection and Associated Thermal Signatures in Southern Highbush Blueberry (Vaccinium corymbosum Interspecific Hybrids)

by Melinda Guzman Martinez, Jonathan E. Oliver and Paul M. Severns

Plants 2023, 12(20), 3562; https://doi.org/10.3390/plants12203562 - 13 Oct 2023

Viewed by 1090

Abstract

Xylella fastidiosa, a gram-negative bacterium vectored to plants via feeding of infected insects, causes a number of notorious plant diseases throughout the world, such as Pierce’s disease (grapes), olive quick decline syndrome, and coffee leaf scorch. Detection of Xf in infected plants can be challenging because the early foliar disease symptoms are subtle and may be attributed to multiple minor physiological stresses and/or borderline nutrient deficiencies. Furthermore, Xf may reside within an infected plant for one or more growing seasons before traditional visible diagnostic disease symptoms emerge. Any method that can identify infection during the latent period or pre-diagnostic disease progress state could substantially improve the outcome of disease control interventions. Because Xf locally and gradually impairs water movement through infected plant stems and leaves over time, infected plants may not be able to effectively dissipate heat through transpiration-assisted cooling, and this heat signature may be an important pre-diagnostic disease trait. Here, we report on the association between thermal imaging, the early stages of Xf infection, and disease development in blueberry plants, and discuss the benefits and limitations of using thermal imaging to detect bacterial leaf scorch of blueberries. Full article

(This article belongs to the Special Issue Emerging Technologies and Tools for Next-Generation Plant Growth Management)

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16 pages, 5089 KiB

Open AccessArticle

Lightweight Detection System with Global Attention Network (GloAN) for Rice Lodging

by Gaobi Kang, Jian Wang, Fanguo Zeng, Yulin Cai, Gaoli Kang and Xuejun Yue

Plants 2023, 12(8), 1595; https://doi.org/10.3390/plants12081595 - 10 Apr 2023

Cited by 2 | Viewed by 1190

Abstract

Rice lodging seriously affects rice quality and production. Traditional manual methods of detecting rice lodging are labour-intensive and can result in delayed action, leading to production loss. With the development of the Internet of Things (IoT), unmanned aerial vehicles (UAVs) provide imminent assistance for crop stress monitoring. In this paper, we proposed a novel lightweight detection system with UAVs for rice lodging. We leverage UAVs to acquire the distribution of rice growth, and then our proposed global attention network (GloAN) utilizes the acquisition to detect the lodging areas efficiently and accurately. Our methods aim to accelerate the processing of diagnosis and reduce production loss caused by lodging. The experimental results show that our GloAN can lead to a significant increase in accuracy with negligible computational costs. We further tested the generalization ability of our GloAN and the results show that the GloAN generalizes well in peers’ models (Xception, VGG, ResNet, and MobileNetV2) with knowledge distillation and obtains the optimal mean intersection over union (mIoU) of 92.85%. The experimental results show the flexibility of GloAN in rice lodging detection. Full article

(This article belongs to the Special Issue Emerging Technologies and Tools for Next-Generation Plant Growth Management)

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16 pages, 5237 KiB

Open AccessArticle

A Wireless Acoustic Emission Sensor System with ACMD-IGWO-XGBoost Algorithm for Living Tree Moisture Content Diagnosis

by Zenan Yang, Yin Wu and Yanyi Liu

Plants 2023, 12(3), 601; https://doi.org/10.3390/plants12030601 - 29 Jan 2023

Viewed by 1584

Abstract

Trunk water has an important influence on the metabolism and ecological balance of living trees, which affects the vegetation growth and moisture cycle of the whole forest ecosystem. The accurate and real-time measurement of moisture content (MC) is of vital guiding meaning to living tree cultivation and forest management. In this paper, a water content diagnosis system based on a wireless acoustic emission sensor network (WASN) was designed and implemented with the aim of the nondestructive detection of water content in living wood trunks. Firstly, the acoustic emission (AE) signal of the trunk epidermis was sampled at high speed; then, its characteristic parameters were calculated and transmitted wirelessly to the gateway. Furthermore, the optimal characteristic wavelet sequence was decomposed by the adaptive chirp mode decomposition (ACMD), and the improved grey wolf optimizer (IGWO) optimization XGBoost established the MC prediction model, which was improved by the multi-strategy joint optimization. Finally, field monitoring was carried out on Robinia Pseudoacacia, Photinia serrulata, Pinus massoniana and Toona sinensis. The average diagnostic accuracy reached 96.75%, which shows that the diagnosis system has excellent applicability in different working conditions. Full article

(This article belongs to the Special Issue Emerging Technologies and Tools for Next-Generation Plant Growth Management)

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50 pages, 16777 KiB

Open AccessArticle

An Analytical Framework on Utilizing Various Integrated Multi-Trophic Scenarios for Basil Production

by Ștefan-Mihai Petrea, Ira Adeline Simionov, Alina Antache, Aurelia Nica, Lăcrămioara Oprica, Anca Miron, Cristina Gabriela Zamfir, Mihaela Neculiță, Maricel Floricel Dima and Dragoș Sebastian Cristea

Plants 2023, 12(3), 540; https://doi.org/10.3390/plants12030540 - 25 Jan 2023

Cited by 2 | Viewed by 1971

Abstract

Here, we aim to improve the overall sustainability of aquaponic basil (Ocimum basilicum L.)-sturgeon (Acipenser baerii) integrated recirculating systems. We implement new AI methods for operational management together with innovative solutions for plant growth bed, consisting of Rapana venosa shells (R), considered wastes in the food processing industry. To this end, the ARIMA-supervised learning method was used to develop solutions for forecasting the growth of both fish and plant biomass, while multi-linear regression (MLR), generalized additive models (GAM), and XGBoost were used for developing black-box virtual sensors for water quality. The efficiency of the new R substrate was evaluated and compared to the consecrated light expended clay aggregate—LECA aquaponics substrate (H). Considering two different technological scenarios (A—high feed input, B—low feed input, respectively), nutrient reduction rates, plant biomass growth performance and additionally plant quality are analysed. The resulting prediction models reveal a good accuracy, with the best metrics for predicting N-NO₃ concentration in technological water. Furthermore, PCA analysis reveals a high correlation between water dissolved oxygen and pH. The use of innovative R growth substrate assured better basil growth performance. Indeed, this was in terms of both average fresh weight per basil plant, with 22.59% more at AR compared to AH, 16.45% more at BR compared to BH, respectively, as well as for average leaf area (LA) with 8.36% more at AR compared to AH, 9.49% more at BR compared to BH. However, the use of R substrate revealed a lower N-NH₄ and N-NO₃ reduction rate in technological water, compared to H-based variants (19.58% at AR and 18.95% at BR, compared to 20.75% at AH and 26.53% at BH for N-NH₄; 2.02% at AR and 4.1% at BR, compared to 3.16% at AH and 5.24% at BH for N-NO₃). The concentration of Ca, K, Mg and NO₃ in the basil leaf area registered the following relationship between the experimental variants: AR > AH > BR > BH. In the root area however, the NO₃ were higher in H variants with low feed input. The total phenolic and flavonoid contents in basil roots and aerial parts and the antioxidant activity of the methanolic extracts of experimental variants revealed that the highest total phenolic and flavonoid contents were found in the BH variant (0.348% and 0.169%, respectively in the roots, 0.512% and 0.019%, respectively in the aerial parts), while the methanolic extract obtained from the roots of the same variant showed the most potent antioxidant activity (89.15%). The results revealed that an analytical framework based on supervised learning can be successfully employed in various technological scenarios to optimize operational management in an aquaponic basil (Ocimum basilicum L.)-sturgeon (Acipenser baerii) integrated recirculating systems. Also, the R substrate represents a suitable alternative for replacing conventional aquaponic grow beds. This is because it offers better plant growth performance and plant quality, together with a comparable nitrogen compound reduction rate. Future studies should investigate the long-term efficiency of innovative R aquaponic growth bed. Thus, focusing on the application of the developed prediction and forecasting models developed here, on a wider range of technological scenarios. Full article

(This article belongs to the Special Issue Emerging Technologies and Tools for Next-Generation Plant Growth Management)

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22 pages, 6193 KiB

Open AccessArticle

Effect of Duration of LED Lighting on Growth, Photosynthesis and Respiration in Lettuce

by Lyubov Yudina, Ekaterina Sukhova, Ekaterina Gromova, Maxim Mudrilov, Yuriy Zolin, Alyona Popova, Vladimir Nerush, Anna Pecherina, Andrey A. Grishin, Artem A. Dorokhov and Vladimir Sukhov

Plants 2023, 12(3), 442; https://doi.org/10.3390/plants12030442 - 18 Jan 2023

Cited by 8 | Viewed by 2652

Abstract

Parameters of illumination including the spectra, intensity, and photoperiod play an important role in the cultivation of plants under greenhouse conditions, especially for vegetables such as lettuce. We previously showed that illumination by a combination of red, blue, and white LEDs with a high red light intensity, was optimal for lettuce cultivation; however, the effect of the photoperiod on lettuce cultivation was not investigated. In the current work, we investigated the influence of photoperiod on production (total biomass and dry weight) and parameters of photosynthesis, respiration rate, and relative chlorophyll content in lettuce plants. A 16 h (light):8 h (dark) illumination regime was used as the control. In this work, we investigated the effect of photoperiod on total biomass and dry weight production in lettuce plants as well as on photosynthesis, respiration rate and chlorophyll content. A lighting regime 16:8 h (light:dark) was used as control. A shorter photoperiod (8 h) decreased total biomass and dry weight in lettuce, and this effect was related to the suppression of the linear electron flow caused by the decreasing content of chlorophylls and, therefore, light absorption. A longer photoperiod (24 h) increased the total biomass and dry weight, nevertheless an increase in photosynthetic processes, light absorption by leaves and chlorophyll content was not recorded, nor were differences in respiration rate, thus indicating that changes in photosynthesis and respiration are not necessary conditions for stimulating plant production. A simple model to predict plant production was also developed to address the question of whether increasing the duration of illumination stimulates plant production without inducing changes in photosynthesis and respiration. Our results indicate that increasing the duration of illumination can stimulate dry weight accumulation and that this effect can also be induced using the equal total light integrals for day (i.e., this stimulation can be also caused by increasing the light period while decreasing light intensity). Increasing the duration of illumination is therefore an effective approach to stimulating lettuce production under artificial lighting. Full article

(This article belongs to the Special Issue Emerging Technologies and Tools for Next-Generation Plant Growth Management)

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19 pages, 10270 KiB

Open AccessArticle

An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse

by Neelesh Sharma, Bikram Pratap Banerjee, Matthew Hayden and Surya Kant

Plants 2023, 12(2), 317; https://doi.org/10.3390/plants12020317 - 09 Jan 2023

Cited by 1 | Viewed by 2283

Abstract

Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping. Full article

(This article belongs to the Special Issue Emerging Technologies and Tools for Next-Generation Plant Growth Management)

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21 pages, 5687 KiB

Open AccessArticle

Biogenic CuO and ZnO Nanoparticles as Nanofertilizers for Sustainable Growth of Amaranthus hybridus

by Dali Vilma Francis, Neeru Sood and Trupti Gokhale

Plants 2022, 11(20), 2776; https://doi.org/10.3390/plants11202776 - 19 Oct 2022

Cited by 10 | Viewed by 3184

Abstract

The biogenic synthesis of CuO and ZnO nanoparticles (NPs) was carried out by Stenotrophomonas maltophilia. The shape, size, and chemical identity of the CuO and ZnO NPs were determined using FTIR, XRD, SEM, EDX, and TEM analysis. The study aimed to investigate the effects of the CuO and ZnO NPs on Amaranthus hybridus seed germination and plant growth. Two different fertilizer application modes (hydroponics and foliar) were studied with varying concentrations of CuO (0.06 µM, 0.12 µM) and ZnO (0.12 µM, 0.24 µM) nanoparticles with water control and Hoagland’s media control. The hydroponic system of fertilizer application demonstrated better efficiency in terms of plant growth as compared to the foliar application. The agronomic traits, SPAD value, total reducing sugars, antioxidant activity, amount of copper, and zinc ions in root and shoot were analyzed for all experimental plants and found better with the nanoparticle application. The highlight of the study is the application of extremely low concentrations of CuO and ZnO nanoparticles, almost 70% lower than the copper and zinc salts in the Hoagland’s medium for improved plant growth. The use of lower concentrations of nanoparticles can prevent their accumulation in the environment and also lower the production cost. The high antioxidant concentration exhibited by the plants treated with CuO and ZnO nanoparticles ensures the enhanced plant’s resistance to infections and pests while promoting plant growth. Full article

(This article belongs to the Special Issue Emerging Technologies and Tools for Next-Generation Plant Growth Management)

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