Precision Agriculture Technologies for Crop Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 7439

Special Issue Editors


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Guest Editor
Center for Advanced Innovation in Agriculture (CAIA), Virginia Tech, Blacksburg, VA 24061, USA
Interests: precision agriculture; data technology; agricultural cybernetics; remote sensing; agricultural automation

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Guest Editor
School of Plant and Environment Sciences, Virginia Tech, Blacksburg, VA 24061, USA
Interests: plant pathology; precision plant protection; remote disease diagnosis; non-invasive crop sensing; precision spraying

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Guest Editor
Tidewater Agricultural Research and Extension Center, Virginia Tech, Suffolk 23437, USA
Interests: high throughput plant breeding; specialty crops; integrated precision crop protection; controlled agriculture; crop inventory management using UAVs

Special Issue Information

Dear Colleagues,

As the global population is projected to double by 2050, researchers and crop producers have just 27 years to transform conventional crop production and management practices. Amid the scarcity of natural resources, demands for chemical-free food, and concerns over environmental sustainability, targeted crop management practices are deemed essential. The advent of precision agriculture technologies can play a critical role in alleviating the mentioned concerns to ultimately achieve food security and sustainability. Precision agriculture is a strategy of collecting, processing, and analyzing spatiotemporal data and combining it with other systems to derive and implement specific production management decisions such as the application of water, growth regulators, fungicides, and insecticides, among others. Importantly, such decisions and implementations need to be equitable and accessible to all grower scales (small-, mid-, and large-sized farming). Therefore, this Special Issue invites cutting-edge and highly beneficial research, reviews, and short technical communication articles on the advances of precision agriculture technologies. These include but are not limited to: integrated sensors and existing sensor applications, cyber–physical systems, internet-of-things, robotics and automated vehicles, unoccupied aerial vehicles, proximal and satellite-based sensing, computer vision and artificial intelligence, big data, cyber-biosecurity, cloud computing, and variable rate applicator technologies. Studies focused on all agricultural domains, including but not limited to agronomy, pathology, horticulture, urban and controlled environment, forestry, aquaculture, and livestock farming, are welcome. Example topics addressed in this issue include, but are not limited to advances in:    

  • New technologies for enhancing the efficiency and capacity of agroecosystem mitigation and management through crop identification, environmental impact assessment, energy sources, adoption studies, and socio-economic assessments.
  • Abiotic and biotic stress mitigation and management.
  • Identification and the development of operational farm zones based on informed decisions on soil health and water management.
  • Site and time-specific soil preparation, monitoring, and health management.
  • Variable rate seeding, pest/disease/weed management, irrigation, and harvesting.
  • Aquaculture and livestock health monitoring and management.
  • Post-harvest storage management, sorting or grading, infection identification, and product quality assessment.
  • Algorithms and software, development and deployment on machinery, computers, smartphones, clouds, and others.
  • Process modeling and validation using field data and theoretical models in real production environments.

Dr. Abhilash Chandel
Dr. David McCall
Prof. Dr. Matthew Chappell
Guest Editors

Manuscript Submission Information

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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. Agriculture 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 2600 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

  • agriculture
  • sensors and imagers
  • algorithms
  • automated systems
  • big data
  • cloud computing
  • variable rate application
  • farm-scale neutral solutions

Published Papers (5 papers)

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Research

18 pages, 1826 KiB  
Article
Phenotyping Peanut Drought Stress with Aerial Remote-Sensing and Crop Index Data
by Maria Balota, Sayantan Sarkar, Rebecca S. Bennett and Mark D. Burow
Agriculture 2024, 14(4), 565; https://doi.org/10.3390/agriculture14040565 - 2 Apr 2024
Viewed by 796
Abstract
Peanut (Arachis hypogaea L.) plants respond to drought stress through changes in morpho-physiological and agronomic characteristics that breeders can use to improve the drought tolerance of this crop. Although agronomic traits, such as plant height, lateral growth, and yield, are easily measured, [...] Read more.
Peanut (Arachis hypogaea L.) plants respond to drought stress through changes in morpho-physiological and agronomic characteristics that breeders can use to improve the drought tolerance of this crop. Although agronomic traits, such as plant height, lateral growth, and yield, are easily measured, they may have low heritability due to environmental dependencies, including the soil type and rainfall distribution. Morpho-physiological characteristics, which may have high heritability, allow for optimal genetic gain. However, they are challenging to measure accurately at the field scale, hindering the confident selection of drought-tolerant genotypes. To this end, aerial imagery collected from unmanned aerial vehicles (UAVs) may provide confident phenotyping of drought tolerance. We selected a subset of 28 accessions from the U.S. peanut mini-core germplasm collection for in-depth evaluation under well-watered (rainfed) and water-restricted conditions in 2018 and 2019. We measured morpho-physiological and agronomic characteristics manually and estimated them from aerially collected vegetation indices. The peanut genotype and water regime significantly (p < 0.05) affected all the plant characteristics (RCC, SLA, yield, etc.). Manual and aerial measurements correlated with r values ranging from 0.02 to 0.94 (p < 0.05), but aerially estimated traits had a higher broad sense heritability (H2) than manual measurements. In particular, CO2 assimilation, stomatal conductance, and transpiration rates were efficiently estimated (R2 ranging from 0.76 to 0.86) from the vegetation indices, indicating that UAVs can be used to phenotype drought tolerance for genetic gains in peanut plants. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Crop Management)
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19 pages, 2257 KiB  
Article
Autonecrotic Tomato (Solanum lycopersicum L.) Line as a Potential Model for Applications in Proximal Sensing of Biotic and Abiotic Stress
by Enrico Santangelo, Angelo Del Giudice, Simone Figorilli, Simona Violino, Corrado Costa, Marco Bascietto, Simone Bergonzoli and Claudio Beni
Agriculture 2024, 14(1), 136; https://doi.org/10.3390/agriculture14010136 - 16 Jan 2024
Viewed by 954
Abstract
The autonecrotic tomato line V20368 (working code IGSV) spontaneously develops necrotic lesions with acropetal progression in response to an increase in temperature and light irradiation. The process is associated with the interaction between tomato and Cladosporium fulvum, the fungal agent of leaf [...] Read more.
The autonecrotic tomato line V20368 (working code IGSV) spontaneously develops necrotic lesions with acropetal progression in response to an increase in temperature and light irradiation. The process is associated with the interaction between tomato and Cladosporium fulvum, the fungal agent of leaf mold. The contemporary presence of an in-house allele encoding the Rcr3lyc protein and the resistance gene Cf-2pim (from Solanum pimpinellifolium) causes auto-necrosis on the leaves even in the absence of the pathogen (hybrid necrosis). The aim of the work was (i) to examine the potential value of the necrotic genotype as a model system for setting up theoretical guidance for monitoring the phytosanitary status of tomato plants and (ii) to develop a predictive model for the early detection of pathogens (or other stresses) in the tomato or other species. Eighteen IGSV tomato individuals at the 4–5th true-leaf stage were grown in three rows (six plants per row) considered to be replicates. The healthy control was the F1 hybrid Elisir (Olter). A second mutant line (SA410) deriving from a cross between the necrotic mutant and a mutant line of the lutescent (l) gene was used during foliar analysis via microspectrometry. The leaves of the mutants and normal plants were monitored through a portable VIS/NIR spectrometer SCIOTM (Consumer Physics, Tel Aviv, Israel) covering a spectral range between 740 and 1070 nm. Two months after the transplant, the acropetal progression of the autonecrosis showed three symptomatic areas (basal, median, apical) on each IGSV plant: necrotic, partially damaged, and green, respectively. Significantly lower chlorophyll content was found in the basal and median areas of IGSV compared with the control (Elisir). A supervised classification/modelling method (SIMCA) was used. Applying the SIMCA model to the dataset of 162 tomato samples led to the identification of the boundary between the healthy and damaged samples (translational critical distance). Two 10 nm wavelength ranges centred at 865 nm and 1055 nm exhibited a stronger link between symptomatology and spectral reflectance. Studies on specific highly informative mutants of the type described may allow for the development of predictive models for the early detection of pathogens (or other stresses) via proximal sensing. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Crop Management)
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11 pages, 551 KiB  
Article
Using Aerial Spectral Indices to Determine Fertility Rate and Timing in Winter Wheat
by Joseph Oakes, Maria Balota, Alexandre-Brice Cazenave and Wade Thomason
Agriculture 2024, 14(1), 95; https://doi.org/10.3390/agriculture14010095 - 3 Jan 2024
Viewed by 1281
Abstract
Tiller density is indicative of the overall health of winter wheat (Triticum aestivum L.) and is used to determine in-season nitrogen (N) application. If tiller density exceeds 538 tillers per m2 at GS 25, then an N application at that stage [...] Read more.
Tiller density is indicative of the overall health of winter wheat (Triticum aestivum L.) and is used to determine in-season nitrogen (N) application. If tiller density exceeds 538 tillers per m2 at GS 25, then an N application at that stage is not needed, only at GS 30. However, it is often difficult to obtain an accurate representation of tiller density across an entire field. Normalized difference vegetative index (NDVI) and normalized difference red edge (NDRE) have been significantly correlated with tiller density when collected from the ground. With the advent of unmanned aerial vehicles (UAVs) and their ability to collect NDVI and NDRE from the air, this study was established to examine the relationship between NDVI, NDRE, and tiller density, and to verify whether N could be applied based on these two indices. From 2018 to 2020, research trials were established across Virginia to develop a model describing the relationship between aerial NDVI, aerial NDRE, and tiller density counted on the ground, in small plots. In 2021, the model was used to apply N in small plots at two locations, where the obtained grain yield was the same whether N was applied based on tiller density, NDVI, or NDRE. From 2022 to 2023, the model was applied at six locations across the state on large scale growers’ fields to compare the amount of GS 25 N recommended by tiller density, NDVI, and NDRE. At three locations, NDVI and NDRE recommended the same N rates as the tiller density method, while at two locations, NDVI and NDRE recommended less N than tiller density. At one location, NDVI and NDRE recommended more N than tiller density. However, across all six locations, there was no difference in grain yield whether N was applied based on tiller density, NDVI, or NDRE. This study indicated that UAV-based NDVI and NDRE are excellent proxies for tiller density determination, and can be used to accurately and economically apply N at GS 25 in winter wheat production. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Crop Management)
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22 pages, 2223 KiB  
Article
Lightweight One-Stage Maize Leaf Disease Detection Model with Knowledge Distillation
by Yanxin Hu, Gang Liu, Zhiyu Chen, Jiaqi Liu and Jianwei Guo
Agriculture 2023, 13(9), 1664; https://doi.org/10.3390/agriculture13091664 - 23 Aug 2023
Cited by 3 | Viewed by 1685
Abstract
Maize is one of the world’s most important crops, and maize leaf diseases can have a direct impact on maize yields. Although deep learning-based detection methods have been applied to maize leaf disease detection, it is difficult to guarantee detection accuracy when using [...] Read more.
Maize is one of the world’s most important crops, and maize leaf diseases can have a direct impact on maize yields. Although deep learning-based detection methods have been applied to maize leaf disease detection, it is difficult to guarantee detection accuracy when using a lightweight detection model. Considering the above problems, we propose a lightweight detection algorithm based on improved YOLOv5s. First, the Faster-C3 module is proposed to replace the original CSP module in YOLOv5s, to significantly reduce the number of parameters in the feature extraction process. Second, CoordConv and improved CARAFE are introduced into the neck network, to improve the refinement of location information during feature fusion and to refine richer semantic information in the downsampling process. Finally, the channel-wise knowledge distillation method is used in model training to improve the detection accuracy without increasing the number of model parameters. In a maize leaf disease detection dataset (containing five leaf diseases and a total of 12,957 images), our proposed algorithm had 15.5% less parameters than YOLOv5s, while the mAP(0.5) and mAP(0.5:0.95) were 3.8% and 1.5% higher, respectively. The experiments demonstrated the effectiveness of the method proposed in this study and provided theoretical and technical support for the automated detection of maize leaf diseases. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Crop Management)
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18 pages, 4837 KiB  
Article
A Cost-Effective Portable Multiband Spectrophotometer for Precision Agriculture
by Francisco Javier Fernández-Alonso, Zulimar Hernández and Vicente Torres-Costa
Agriculture 2023, 13(8), 1467; https://doi.org/10.3390/agriculture13081467 - 25 Jul 2023
Viewed by 1117
Abstract
The United Nations marks responsible consumption and production as one of the 17 key goals to fulfill the 2030 Agenda for Sustainable Development. In this context, affordable precision instruments can play a significant role in the optimization of crops in developing countries where [...] Read more.
The United Nations marks responsible consumption and production as one of the 17 key goals to fulfill the 2030 Agenda for Sustainable Development. In this context, affordable precision instruments can play a significant role in the optimization of crops in developing countries where precision agriculture tools are barely available. In this work, a simple to use, cost-effective instrument for spectral analysis of plants and fruits based on open-source hardware and software has been developed. The instrument is a 7-band spectrophotometer equipped with a microprocessor that allows one to acquire the reflectance spectrum of samples and compute up to 9 vegetation indices. The accuracy in reflectance measurements is between 0.4% and 1.4% full scale, just above that of high-end spectrophotometers, while the precision at determining the normalized difference vegetation index (NDVI) is 0.61%, between 3 and 6 times better than more expensive commercial instruments. Some use cases of this instrument have been tested, and the prototype has proven to be able to precisely monitor minute spectral changes of different plants and fruits under different conditions, most of them before they were perceptible to the bare eye. This kind of information is essential in the decision-making process regarding harvesting, watering, or pest control, allowing precise control of crops. Given the low cost (less than USD 100) and open-source architecture of this instrument, it is an affordable tool to bring precision agriculture techniques to small farmers in developing countries. Full article
(This article belongs to the Special Issue Precision Agriculture Technologies for Crop Management)
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