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Agriculture
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Latest Advances for Smart and Sustainable Agriculture
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Latest Advances for Smart and Sustainable Agriculture

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

Deadline for manuscript submissions: closed (20 August 2021) | Viewed by 37337

Special Issue Editors

Dr. Selma Boumerdassi

E-Mail Website
Guest Editor
CNAM/CEDRIC, 292 rue Saint Martin, 75003 Paris, France
Interests: networks; energy-efficient devices; IoT; pollution and environmental issues
Prof. Dr. Eric Renault

E-Mail Website
Guest Editor
LIGM, University Gustave Eiffel, CNRS, ESIEE Paris, 93162 Noisy-le-Grand, France
Interests: communication; security; machine learning; environmental issues
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christopher Robin Bryant

E-Mail Website
Guest Editor
School of Environmental Design and Rural Development, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: the adaptation of human activities to climatic change, especially agriculture; sustainable community development; rural development; land use planning; strategic management/planning of development including agriculture; community participation; the dynamics and planning of urban agriculture; including pioneer work on adaptation behavior under stressful conditions; sustainable transport policies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agricultural sector has always been a major user of water and fossil fuels, thus emitting a considerable amount of greenhouse gases. It was with the purpose of solving this problem in a sustainable way that the concept of smart agriculture was proposed. This essentially aims to achieve three major objectives that are closely linked: 1) ensuring and improving food security through agricultural production; 2) promoting the resilience of agriculture by adapting to climatic conditions; 3) reducing greenhouse gas emissions in line with the principle of mitigation.

This concept must be put into practice in different ways, depending on the development of the agricultural techniques used and the specific realities of each country. Developing countries must demonstrate innovation in the technical and energy fields in order to better reconcile their adaptation and mitigation capacities. In developed countries, the innovations available should enable the development of more qualitative than quantitative agriculture.

The objective of this Special Issue is to present the latest advances in smart and sustainable agriculture, in particular in terms of new information and telecommunications technologies.

Dr. Selma Boumerdassi
Prof. Dr. Eric Renault
Prof. Dr. Christopher Robin Bryant
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

  • Data processing for smart agriculture
  • Standards and norms
  • Security and privacy
  • Machine learning and big data
  • Application for smart agriculture
  • Autonomous systems
  • Image processing
  • Testbeds and platforms
  • Robotics and energy efficient devices
  • Renewable-energy based devices
  • Low-cost solutions for wide-area exploitations and developing countries
  • Smart agriculture and urban farming
  • Smart irrigation
  • Application to small-size and large-size exploitations
  • Application of ancestral farming to smart agriculture
  • Waste management for agriculture 2.0
  • Census of regional ancestral farming

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Published Papers (7 papers)

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Research

17 pages, 1577 KiB  
Article
Anomaly Detection on Data Streams for Smart Agriculture
by Juliet Chebet Moso, Stéphane Cormier, Cyril de Runz, Hacène Fouchal and John Mwangi Wandeto
Agriculture 2021, 11(11), 1083; https://doi.org/10.3390/agriculture11111083 - 2 Nov 2021
Cited by 24 | Viewed by 4748
Abstract
Smart agriculture technologies are effective instruments for increasing farm sustainability and production. They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and efficiency. In this context, the detection of anomalies can help in [...] Read more.
Smart agriculture technologies are effective instruments for increasing farm sustainability and production. They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and efficiency. In this context, the detection of anomalies can help in the identification of observations that deviate from the norm. This paper proposes an adaptation of an ensemble anomaly detector called enhanced locally selective combination in parallel outlier ensembles (ELSCP). On this basis, we define an unsupervised data-driven methodology for smart-farming temporal data that is applied in two case studies. The first considers harvest data including combine-harvester Global Positioning System (GPS) traces. The second is dedicated to crop data where we study the link between crop state (damaged or not) and detected anomalies. Our experiments show that our methodology achieved interesting performance with Area Under the Curve of Precision-Recall (AUCPR) score of 0.972 in the combine-harvester dataset, which is 58.7% better than that of the second-best approach. In the crop dataset, our analysis showed that 30% of the detected anomalies could be directly linked to crop damage. Therefore, anomaly detection could be integrated in the decision process of farm operators to improve harvesting efficiency and crop health. Full article
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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18 pages, 1208 KiB  
Article
Dynamics of Coffee Certifications in Producer Countries: Re-Examining the Tanzanian Status, Challenges and Impacts on Livelihoods and Environmental Conservation
by Joseph Rajabu Kangile, Reuben M. J. Kadigi, Charles Peter Mgeni, Bernadetha Pantaleo Munishi, Japhet Kashaigili and Pantaleo K. T. Munishi
Agriculture 2021, 11(10), 931; https://doi.org/10.3390/agriculture11100931 - 27 Sep 2021
Cited by 6 | Viewed by 3588
Abstract
Certification is increasingly becoming necessary for accessing coffee export markets and practicing environmental conservation, especially at this time when many of the farmers in developing countries strive to achieve agricultural transformation. Using data from 400 randomly selected coffee farmers in Tanzania, the study [...] Read more.
Certification is increasingly becoming necessary for accessing coffee export markets and practicing environmental conservation, especially at this time when many of the farmers in developing countries strive to achieve agricultural transformation. Using data from 400 randomly selected coffee farmers in Tanzania, the study determined the status, constraints, key drivers, and impact of coffee certifications. Descriptive statistics and the endogenous switching regression (ESR) model were used for data analysis. Results indicated that the level of coffee certification is low, being constrained by unawareness and inaccessibility, the prevalence of coffee diseases, failure in realizing price advantages, and certification not being cost effective. Economies of scale, experience, and participation in collective actions are significant factors affecting coffee farmers’ decision to join certification schemes. Additionally, the study rejects the hypothesis of certification to improve household income. However, certification improved awareness and practices of environmental conservation among coffee farmers. It is thus important to embark on awareness creation and make certification services accessible and cost effective to coffee farmers for increased access to niche export markets. Easing transmission of price premiums to coffee farmers will also increase the supply of sustainably grown coffee, improve coffee farmers’ livelihood, and help in the attainment of environmental sustainability goals within the coffee supply chain. Full article
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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16 pages, 6605 KiB  
Article
Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ
by Kamal KC, Zhendong Yin, Dasen Li and Zhilu Wu
Agriculture 2021, 11(9), 827; https://doi.org/10.3390/agriculture11090827 - 30 Aug 2021
Cited by 22 | Viewed by 6327
Abstract
Convolutional neural networks have an immense impact on computer vision tasks. However, the accuracy of convolutional neural networks on a dataset is tremendously affected when images within the dataset highly vary. Test images of plant leaves are usually taken in situ. These images, [...] Read more.
Convolutional neural networks have an immense impact on computer vision tasks. However, the accuracy of convolutional neural networks on a dataset is tremendously affected when images within the dataset highly vary. Test images of plant leaves are usually taken in situ. These images, apart from the region of interest, contain unwanted parts of plants, soil, rocks, and/or human body parts. Segmentation helps isolate the target region and a deep convolutional neural network classifies images precisely. Therefore, we combined edge and morphological based segmentation, background subtraction, and the convolutional neural network to help improve accuracy on image sets with images containing clean and cluttered backgrounds. In the proposed system, segmentation was applied to first extract leaf images in the foreground. Several images contained a leaf of interest interposed between unfavorable foregrounds and backgrounds. Background subtraction was implemented to remove the foreground image followed by segmentation to obtain the region of interest. Finally, the images were classified by a pre-trained classification network. The experimental results on two, four, and eight classes of datasets show that the proposed method achieves 98.7%, 96.7%, and 93.57% accuracy by fine-tuned DenseNet121, InceptionV3, and DenseNet121 models, respectively, on a clean dataset. For two class datasets, the accuracy obtained was about 12% higher for a dataset with images taken in the homogeneous background compared to that of a dataset with testing images with a cluttered background. Results also suggest that image sets with clean backgrounds tend to start training with higher accuracy and converge faster. Full article
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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11 pages, 2585 KiB  
Article
Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling
by Agnieszka A. Pilarska, Piotr Boniecki, Małgorzata Idzior-Haufa, Maciej Zaborowicz, Krzysztof Pilarski, Andrzej Przybylak and Hanna Piekarska-Boniecka
Agriculture 2021, 11(8), 732; https://doi.org/10.3390/agriculture11080732 - 1 Aug 2021
Cited by 7 | Viewed by 3222
Abstract
Quality evaluation of products is a critical stage in the process of production. It also applies to the production of beer and its main ingredients, i.e., hops, yeast, malting barley and other components. The research described in this paper deals with the multifaceted [...] Read more.
Quality evaluation of products is a critical stage in the process of production. It also applies to the production of beer and its main ingredients, i.e., hops, yeast, malting barley and other components. The research described in this paper deals with the multifaceted quality evaluation of malting barley needed for the production of malt. The project aims to elaborate on the original methodology used for identifying grain varieties, grain contamination degree and other visual characteristics of malting barley employing new computer technologies, including artificial intelligence (AI) and neural image analysis. The neural modelling and digital image analysis assist in identifying the quality of barley varieties. According to the study, information concerning the colour of barley varieties presented in digital images is sufficient for this purpose. The multi-layer perceptron (MLP)-type neural network generated using a data set describing the colour of kernels presented in digital images was the best model for recognising the analysed malting barley varieties. The proposed procedure may bring specific benefits to malthouses, influencing the beer production quality in the future. Full article
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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16 pages, 36693 KiB  
Article
Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning
by Jie Xu, Suri Guga, Guangzhi Rong, Dao Riao, Xingpeng Liu, Kaiwei Li and Jiquan Zhang
Agriculture 2021, 11(7), 607; https://doi.org/10.3390/agriculture11070607 - 29 Jun 2021
Cited by 13 | Viewed by 3487
Abstract
Tea trees are the main economic crop in Zhejiang Province. However, spring cold is a frequent occurrence there, causing frost damage to the valuable tea buds. To address this, a regional frost-hazard early-warning system is needed. In this study, frost damage area was [...] Read more.
Tea trees are the main economic crop in Zhejiang Province. However, spring cold is a frequent occurrence there, causing frost damage to the valuable tea buds. To address this, a regional frost-hazard early-warning system is needed. In this study, frost damage area was estimated based on topography and meteorology, as well as longitude and latitude. Based on support vector machine (SVM) and artificial neural networks (ANNs), a multi-class classification model was proposed to estimate occurrence of regional frost disasters using tea frost cases from 2017. Results of the two models were compared, and optimal parameters were adjusted through multiple iterations. The highest accuracies of the two models were 83.8% and 75%, average accuracies were 79.3% and 71.3%, and Kappa coefficients were 79.1% and 67.37%. The SVM model was selected to establish spatial distribution of spring frost damage to tea trees in Zhejiang Province in 2016. Pearson’s correlation coefficient between prediction results and meteorological yield was 0.79 (p < 0.01), indicating consistency. Finally, the importance of model factors was assessed using sensitivity analysis. Results show that relative humidity and wind speed are key factors influencing accuracy of predictions. This study supports decision-making for hazard prediction and defense for tea trees facing frost. Full article
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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13 pages, 1046 KiB  
Article
Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS
by Saeed Nosratabadi, Sina Ardabili, Zoltan Lakner, Csaba Mako and Amir Mosavi
Agriculture 2021, 11(5), 408; https://doi.org/10.3390/agriculture11050408 - 2 May 2021
Cited by 71 | Viewed by 8239
Abstract
Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer [...] Read more.
Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations. Full article
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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19 pages, 636 KiB  
Article
German Farmers’ Attitudes on Adopting Autonomous Field Robots: An Empirical Survey
by Friedrich Rübcke von Veltheim and Heinke Heise
Agriculture 2021, 11(3), 216; https://doi.org/10.3390/agriculture11030216 - 6 Mar 2021
Cited by 23 | Viewed by 5372
Abstract
Agricultural production methods in Europe are increasingly subject to public criticism from which many farmers suffer. This applies, among other areas, to the widespread use of pesticides. Autonomous field robots (AFR), as the next stage of agricultural automation, have the potential to farm [...] Read more.
Agricultural production methods in Europe are increasingly subject to public criticism from which many farmers suffer. This applies, among other areas, to the widespread use of pesticides. Autonomous field robots (AFR), as the next stage of agricultural automation, have the potential to farm more intensively and, at the same time, in a more environmentally friendly way. However, a certain skepticism towards autonomous systems is suspected among farmers. Whether farmers adopt a technology depends largely on their uncertainty about the consequences of its use and the resulting attitude on the adoption. In order to quantify the attitude on adopting AFR in Germany and to identify possible group differences within the population, 490 German farmers were surveyed using an online questionnaire, which is based on an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT). In the subsequent cluster analysis, the statements inquiring the intention to use AFR served as cluster-forming variables. As a result, three groups (“open-minded AFR supporters”, “convinced AFR adopters”, “reserved AFR interested”) could be identified according to their response behavior. Despite existing group differences, an overall attitude in favor of autonomous field robots was observed. The results complement the existing research with a further empirical study and provide interesting starting points for further analysis, field robot manufacturers, and political decision makers. Full article
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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