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Agriculture
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Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of...
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Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT)

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 45487

Special Issue Editors

Dr. Paul Kwan

E-Mail Website
Guest Editor
School of Engineering and Technology, CQUniversity Brisbane, 160 Ann St., Brisbane City, QLD 4000, Australia
Interests: artificial intelligence; pattern recognition; computer vision; machine learning; computational science; data science; digital agriculture; agroinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wensheng Wang

E-Mail Website
Guest Editor
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 10081, China
Interests: Artificial Intelligence; machine learning; information technology; digital agriculture; agro informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agricultural sector has a rich history of adopting novel technologies to boost productivity, decrease risk, and improve sustainability. There is a growing trend in the use of digital and communications technologies by farmers and policy makers alike to address issues that have arisen due to climate change, scarcity of resources, a rising global population, disruption of supply chains by the pandemic and natural disasters, etc., which hamper efficiency and significantly impact business models in the agricultural sector. The opportunities presented by such technological integration are numerous, but they also come with new and evolving challenges.

In both industry and academia circles, new research and development initiatives have been proposed and undertaken to address many of these issues. Innovations including real-time monitoring and controlling crop irrigation systems via smartphones, crop sensors, intelligent livestock farming technology using UAV-based computer vision and deep AI technologies, farm automation, indoor vertical farming, modern greenhouses, precision agriculture, blockchain for distributed data sharing and commodity exchange, and access to the industrial internet of things through the fog and edge computing, have all benefited the agricultural sector. One shared characteristic of these technologies is the important role that "data" plays in driving their success by achieving the proposed benefits. 

This Special Issue will showcase "data-driven agricultural innovations" across different data scales and resolutions using artificial intelligence and the industrial internet of things (IIoT). We welcome contributions that address topics including, but not limited to key and emergent R&D issues for agricultural innovations across the edge-, fog-, and cloud-layered architectures and computing, hyperspectral and multispectral remote sensing, autonomous robotics and computer vision systems, multisensor data filtering, and fusion, big data processing and analytics, digital twin technology, intelligent logistics and supply chain management, complex systems modeling and simulation, risk assessment, prediction, and decision analysis through applying scientific approaches, and methodologies from multiple disciplines, including artificial intelligence, computer vision, machine learning, robotics, cyber-physical systems, cloud and edge computing, cyber security, blockchain, data science, computational science, operations research, remote sensing, agro informatics, crop science, and animal science.

Prof. Dr. Paul Kwan
Prof. Dr. Wensheng 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. 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

  • precision agriculture
  • smart farming
  • agricultural innovations
  • artificial intelligence
  • internet of things
  • data science
  • machine learning
  • computer vision
  • robotics
  • intelligent systems
  • autonomous systems
  • agro informatics
  • cloud computing
  • edge computing
  • fog computing
  • blockchain
  • cyber-security
  • remote sensing
  • big data analytics
  • digital twin
  • cyber-physical systems
  • multisensor data
  • supply chain
  • computational modeling
  • decision science
  • risk management

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Published Papers (7 papers)

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Research

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18 pages, 2680 KiB  
Article
Leveraging Important Covariate Groups for Corn Yield Prediction
by Britta L. Schumacher, Emily K. Burchfield, Brennan Bean and Matt A. Yost
Agriculture 2023, 13(3), 618; https://doi.org/10.3390/agriculture13030618 - 3 Mar 2023
Cited by 4 | Viewed by 2293
Abstract
Accurate yield information empowers farmers to adapt, their governments to adopt timely agricultural and food policy interventions, and the markets they supply to prepare for production shifts. Unfortunately, the most representative yield data in the US, provided by the US Department of Agriculture, [...] Read more.
Accurate yield information empowers farmers to adapt, their governments to adopt timely agricultural and food policy interventions, and the markets they supply to prepare for production shifts. Unfortunately, the most representative yield data in the US, provided by the US Department of Agriculture, National Agricultural Statistics Service (USDA-NASS) Surveys, are spatiotemporally patchy and inconsistent. This paper builds a more complete data product by examining the spatiotemporal efficacy of random forests (RF) in predicting county-level yields of corn—the most widely cultivated crop in the US. To meet our objective, we compare RF cross-validated prediction accuracy using several combinations of explanatory variables. We also utilize variable importance measures and partial dependence plots to compare and contextualize how key variables interact with corn yield. Results suggest that RF predicts US corn yields well using a relatively small subset of climate variables along with year and geographical location (RMSE = 17.1 bushels/acre (1.2 tons/hectare)). Of note is the insensitivity of RF prediction accuracy when removing variables traditionally thought to be predictive of yield or variables flagged as important by RF variable importance measures. Understanding what variables are needed to accurately predict corn yields provides a template for applying machine learning approaches to estimate county-level yields for other US crops. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))
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24 pages, 6288 KiB  
Article
Dynamic Change in Normalised Vegetation Index (NDVI) from 2015 to 2021 in Dhofar, Southern Oman in Response to the Climate Change
by Khalifa M. Al-Kindi, Rahma Al Nadhairi and Suleiman Al Akhzami
Agriculture 2023, 13(3), 592; https://doi.org/10.3390/agriculture13030592 - 28 Feb 2023
Cited by 11 | Viewed by 3446
Abstract
Climate change poses a major threat to vegetation and land cover worldwide. The loss of vegetation as a result of climate change can alter the functions and structure of the environment and its ecological systems. In the first part of this study, Sentinel-2 [...] Read more.
Climate change poses a major threat to vegetation and land cover worldwide. The loss of vegetation as a result of climate change can alter the functions and structure of the environment and its ecological systems. In the first part of this study, Sentinel-2 data, normalised different vegetation index (NDVI), and multiple regression methods were used to examine the impacts of the climatic factors of humidity, rainfall, and air temperature on vegetation dynamics from 2015 to 2021 in Dhofar, Southern Oman. In the second part of this study, random forest regression was employed to model the relationships between the NDVI and temperature, humidity, rainfall, soil map, geology map, topographic wetness index, curvature, elevation, slope, aspect, distance to buildings, and distance to roads. The multiple regression values revealed significant associations between the spatial distributions of the NDVI and the abovementioned climatic factors. The findings also indicated an increase of 1 °C in air temperature fluctuations between 2018 and 2021 over all five sites, with a strong tendency over Qairoon Hairiti Mountain. The rainfall records clearly indicated an increasing tendency from 2018 to 2020 due to the impact of frequent cyclones. Therefore, the results revealed a significant increase of 0.01 in the vegetation cover trend in 2018, 2019, and 2020 along the Sadah Mountain range and the eastern part of the Jabal Qara Mountains under the areas directly impacted by the cyclone, whereas there was a decrease along the western mountain range consisting of Jabal Qara and Jabal Qamar Mountains due to the impact of warm, dry air. The results revealed that NDVI values were sensitive to heavy rainfall over Jabal Samhan Mountain. The 12 variables that influenced NDVI levels had different levels of importance. Soil types, elevation, slope, rainfall, curvature, humidity, and temperature had the highest importance, while topographic wetness index, distance to urban area, aspect, distance to roads, and geology map had the lowest. The findings provide a significant foundation for Oman’s planning and management of regional vegetation, water conservation, and animal husbandry. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))
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13 pages, 4107 KiB  
Article
Identifying Working Trajectories of the Wheat Harvester In-Field Based on K-Means Algorithm
by Lili Yang, Xinxin Wang, Yuanbo Li, Zhongxiang Xie, Yuanyuan Xu, Rongxin Han and Caicong Wu
Agriculture 2022, 12(11), 1837; https://doi.org/10.3390/agriculture12111837 - 2 Nov 2022
Cited by 7 | Viewed by 1952
Abstract
Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the “turning”, “working”, and “abnormal working” trajectories for wheat harvester in-field operation scenarios. This method contains [...] Read more.
Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the “turning”, “working”, and “abnormal working” trajectories for wheat harvester in-field operation scenarios. This method contains two stages: clustering and correction. The clustering stage performs by the two-step K-means iterative clustering method (D-K-means). In the correction stage, the first step (M1) is performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) is based on the direction change of the “turning” and “abnormal working” trajectories. The third correction step (M3) is based on the operating characteristics to specify the start and stop positions of the turning. The developed method was validated by 50 trajectories. The results for the three trajectories and the five time intervals from 1 s to 5 s both have f1-scores above 0.90, and the f1-score using only the clustering method and the method of this paper increased from 0.55 to 0.95. After removing the turning and abnormal operation trajectories, the error of calculating farmland area with distance algorithm is reduced by 17.04% compared with that before processing. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))
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13 pages, 3070 KiB  
Article
Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery
by Yulin Shen, Benoît Mercatoris, Zhen Cao, Paul Kwan, Leifeng Guo, Hongxun Yao and Qian Cheng
Agriculture 2022, 12(6), 892; https://doi.org/10.3390/agriculture12060892 - 20 Jun 2022
Cited by 36 | Viewed by 4724
Abstract
Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels [...] Read more.
Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short-term memory neural network and random forest (LSTM-RF) was proposed for predicting wheat yield using VIs and CWSI from multi-growth stages as predictors. Validation results showed that the R2 of 0.61 and the RMSE value of 878.98 kg/ha was achieved in predicting grain yield using LSTM. LSTM-RF model obtained better prediction results compared to the LSTM with n R2 of 0.78 and RMSE of 684.1 kg/ha, which is equivalent to a 22% reduction in RMSE. The results showed that LSTM-RF considered both the time-series characteristics of the winter wheat growth process and the non-linear characteristics between remote sensing data and crop yield data, providing an alternative for accurate yield prediction in modern agricultural management. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))
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Review

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26 pages, 3535 KiB  
Review
Leveraging on Advanced Remote Sensing- and Artificial Intelligence-Based Technologies to Manage Palm Oil Plantation for Current Global Scenario: A Review
by Mohammad Nishat Akhtar, Emaad Ansari, Syed Sahal Nazli Alhady and Elmi Abu Bakar
Agriculture 2023, 13(2), 504; https://doi.org/10.3390/agriculture13020504 - 20 Feb 2023
Cited by 7 | Viewed by 5628
Abstract
Advanced remote sensing technologies have undoubtedly revolutionized palm oil industry management by bringing business and environmental benefits on a single platform. It is evident from the ongoing trend that remote sensing using satellite and aerial data is able to provide precise and quick [...] Read more.
Advanced remote sensing technologies have undoubtedly revolutionized palm oil industry management by bringing business and environmental benefits on a single platform. It is evident from the ongoing trend that remote sensing using satellite and aerial data is able to provide precise and quick information for huge palm oil plantation areas using high-resolution image processing, which is also recognized by the certification agencies, i.e., the Roundtable on Sustainable Palm Oil (RSPO) and ISCC (International Sustainability and Carbon Certification). A substantial improvement in the palm oil industry could be attained by utilizing the latest Geo-information tools and technologies equipped with AI (Artificial Intelligence) algorithms and image processing, which could help to identify illegal deforestation, tree count, tree height, and the early detection of diseased leaves. This paper reviews some of the latest technologies equipped with remote sensing, AI, and image processing for managing the palm oil plantation. This manuscript also highlights how the distress in the current palm oil industry could be handled by mentioning some of the improvised monitoring systems for palm oil plantation that could in turn increase the yield of palm oil. It is evident from the proposed review that the accuracy of AI algorithms for palm oil detection depends on various factors such as the quality of the training data, the design of the neural network, and the type of detection task. In general, AI models have achieved high accuracy in detecting palm oil tree images, with some studies reporting accuracy levels up to 91%. However, it is important to note that accuracy can still be affected by factors such as variations in lighting conditions and image resolution. Nonetheless, with any AI model, the accuracy of algorithms for palm oil tree detection can be improved by collecting more diverse training data and fine-tuning the model. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))
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22 pages, 5163 KiB  
Review
Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production
by Awais Ali, Tajamul Hussain, Noramon Tantashutikun, Nurda Hussain and Giacomo Cocetta
Agriculture 2023, 13(2), 397; https://doi.org/10.3390/agriculture13020397 - 8 Feb 2023
Cited by 51 | Viewed by 14548
Abstract
Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand [...] Read more.
Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))
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38 pages, 2250 KiB  
Review
Survey on the Applications of Blockchain in Agriculture
by Krithika L.B.
Agriculture 2022, 12(9), 1333; https://doi.org/10.3390/agriculture12091333 - 29 Aug 2022
Cited by 45 | Viewed by 10551
Abstract
Dating back many millennia, agriculture is an ancient practice in the evolution of civilization. It was developed when humans thought about it and concluded that not everyone in the community was required to produce food. Instead, specialized labor, tools, and techniques could help [...] Read more.
Dating back many millennia, agriculture is an ancient practice in the evolution of civilization. It was developed when humans thought about it and concluded that not everyone in the community was required to produce food. Instead, specialized labor, tools, and techniques could help people achieve surplus food for their community. Since then, agriculture has continuously evolved across the ages and has occupied a vital, synergistic position in the existence of humanity. The evolution of agriculture was based on a compulsion to feed the growing population, and, importantly, maintain the quality and traceability of food, prevent counterfeit products, and modernize and optimize yield. Recent trends and advancements in blockchain technology have some significant attributes that are ideal for agriculture. The invention and implementation of blockchain have caused a fair share of positive disruptions and evolutionary adoption in agriculture to modernize the domain. Blockchain has been adopted at various stages of the agriculture lifecycle for improved evolution. This work presents an intense survey of the literature on how blockchain has positively impacted and continues to influence various market verticals in agriculture, the challenges and the future. Full article
(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))
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