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Advanced Sensing Technologies for Smart Farming and Sustainable Food Production
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Advanced Sensing Technologies for Smart Farming and Sustainable Food Production

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

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 9068

Special Issue Editor

Dr. Wen-Hao Su

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: smart urban agriculture; artificial intelligence; agricultural robotics; automated control; unmanned aerial vehicle; plant phenotyping; computer vision; crop plant signaling; machine (deep) learning; food processing and safety; fluorescence imaging; hyper/multispectral imaging; Vis/NIR/MIR imaging spectroscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture provides human beings with abundant food for our daily needs. Global population growth and rising income levels are driving the need for diverse foods, which may put additional pressure on natural resources. As the demand for nutritious and healthy food further increases, there is a greater awareness of the negative impact of agriculture on the environment. New technologies and methods should be able to meet future food demands while maintaining or reducing the environmental impact of agricultural inputs. Advanced sensing technologies combined with artificial intelligence (AI) facilitate informed management decisions by crop monitoring, disease and pest management, nutrient application, and yield forecasting aimed to increase food production. In smart farms, a series of advanced intelligent sensing technologies need to be applied to optimize agricultural inputs, which can sustainably increase the production of delicious and nutritious foods.

This Special Issue, therefore, aims to put together original research and review articles on recent advances in sensing technologies for smart farming and sustainable food production.

Potential topics include but are not limited to:

  • Smart agriculture;
  • Sensor technology and applications;
  • Sensing and imaging;
  • Data processing;
  • Deep learning;
  • Artificial intelligence;
  • Big data analysis;
  • Internet of Things;
  • Crop management;
  • Disease and pest management;
  • Nutrient management;
  • Weed management;
  • Food quality control.

Dr. Wen-Hao Su
Guest Editor

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. Sensors 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 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.

Published Papers (3 papers)

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Research

16 pages, 4044 KiB  
Article
Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network
by Ke-Jun Fan, Bo-Yuan Liu and Wen-Hao Su
Sensors 2023, 23(5), 2668; https://doi.org/10.3390/s23052668 - 28 Feb 2023
Cited by 1 | Viewed by 1378
Abstract
Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382–1030 nm) in tandem with an [...] Read more.
Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382–1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Smart Farming and Sustainable Food Production)
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21 pages, 54926 KiB  
Article
Designing a Proximal Sensing Camera Acquisition System for Vineyard Applications: Results and Feedback on 8 Years of Experiments
by Florian Rançon, Barna Keresztes, Aymeric Deshayes, Malo Tardif, Florent Abdelghafour, Gael Fontaine, Jean-Pierre Da Costa and Christian Germain
Sensors 2023, 23(2), 847; https://doi.org/10.3390/s23020847 - 11 Jan 2023
Cited by 3 | Viewed by 1657
Abstract
The potential of image proximal sensing for agricultural applications has been a prolific scientific subject in the recent literature. Its main appeal lies in the sensing of precise information about plant status, which is either harder or impossible to extract from lower-resolution downward-looking [...] Read more.
The potential of image proximal sensing for agricultural applications has been a prolific scientific subject in the recent literature. Its main appeal lies in the sensing of precise information about plant status, which is either harder or impossible to extract from lower-resolution downward-looking image sensors such as satellite or drone imagery. Yet, many theoretical and practical problems arise when dealing with proximal sensing, especially on perennial crops such as vineyards. Indeed, vineyards exhibit challenging physical obstacles and many degrees of variability in their layout. In this paper, we present the design of a mobile camera suited to vineyards and harsh experimental conditions, as well as the results and assessments of 8 years’ worth of studies using that camera. These projects ranged from in-field yield estimation (berry counting) to disease detection, providing new insights on typical viticulture problems that could also be generalized to orchard crops. Different recommendations are then provided using small case studies, such as the difficulties related to framing plots with different structures or the mounting of the sensor on a moving vehicle. While results stress the obvious importance and strong benefits of a thorough experimental design, they also indicate some inescapable pitfalls, illustrating the need for more robust image analysis algorithms and better databases. We believe sharing that experience with the scientific community can only benefit the future development of these innovative approaches. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Smart Farming and Sustainable Food Production)
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29 pages, 8839 KiB  
Article
IoT-Based Systems for Soil Nutrients Assessment in Horticulture
by Stefan Postolache, Pedro Sebastião, Vitor Viegas, Octavian Postolache and Francisco Cercas
Sensors 2023, 23(1), 403; https://doi.org/10.3390/s23010403 - 30 Dec 2022
Cited by 12 | Viewed by 5160
Abstract
Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and [...] Read more.
Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Smart Farming and Sustainable Food Production)
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