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Special Issue "Innovative Methods and Technology for Resilience Agrifood Systems"

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

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 7247

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Special Issue Editors

Dr. Alfadhl Alkhaled

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Guest Editor

Department of Horticulture, College of Agricultural & Life Sciences, University of Wisconsin–Madison, Madison, WI 53705, USA
Interests: remote sensing; agriculture engineering; hyperspectral imaging system; spectroscopy technology; smart farming; precision agriculture; machine learning; data science; agricultural informatics and instrumentation

Dr. Akinbode A. Adedeji

E-Mail Website
Guest Editor

Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA
Interests: sustainable food processing; non-destructive methods - hyperspectral imaging, acoustic sensing, machine learning; food extrusion; alternative ingredients/ protein; millet value-addition
Special Issues, Collections and Topics in MDPI journals

Dr. Samsuzana Abd Aziz

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Guest Editor

Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
Interests: agricultural instrumentation; agricultural information technology; agricultural informatics; computational intelligence; precision agriculture

Special Issue Information

Dear Colleagues,

There is no doubt that the world’s growing population urgently needs to develop agricultural and farming techniques to increase productivity to achieve the goal of "Zero Hunger". By November 16, 2022, it is projected that the global population will reach 8 billion, and by 2050, it will increase to about 9.7 billion. The Food and Agriculture Organization (FAO) predicted that worldwide food demand will rise by 50% by 2050. From now till the end of the 21^st century, considering the SDG’s targets, substantial effort and innovation will be required to increase agricultural production and food processing sustainably. Consequently, the development and optimization of sustainable modern technologies are indispensable for changing the landscape of how agrifood production and processing are performed and enhancing the operating capabilities in the agriculture sector. Furthermore, there might be a need to revive traditional agricultural methods with advanced technological techniques that could lead to eco-friendly, sustainable, and efficient agrifood systems.

This Special Issue, “Innovative Methods and Technology for Resilience Agrifood Systems”, aims to cover the recent developments and the latest research in resilience farming systems, agricultural productions, and modern techniques in post-harvest food handling and processing, storage, and aggregation. We specifically target the development and application of novel and innovative technologies that provide insight into sustainable and smart agrifood systems. Contributions are expected to cover techniques such as advanced sensing systems, agricultural automation, and robotics, agrifood electronics, crop innovation technology, information and communication technology, data analytics, and computational intelligence. In addition, review papers providing critical overviews of state-of-the-art developments on smart agrifood systems research are welcomed. Topics include, but are not limited to:

Smart Farming;
Precision Agriculture;
Data-driven Agrifood Systems;
Crop Innovation Technology;
Smart Food Processing and Handling;
Nondestructive methods for Agrifood Quality Assessment.

Dr. Alfadhl Alkhaled
Dr. Akinbode A. Adedeji
Dr. Samsuzana Abd Aziz
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

smart farming
precision agriculture
data-driven agrifood systems
crop innovation technology
smart food processing and handling
nondestructive methods for agrifood quality assessment

Published Papers (5 papers)

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Research

Open AccessArticle

Electron Beam Irradiation to Control Rhizoctonia solani in Potato

Natalya Chulikova

Anna Malyuga

Polina Borshchegovskaya

Alexander Nikitchenko

Ulyana Bliznyuk

and

Igor Rodin

Agriculture 2023, 13(6), 1221; https://doi.org/10.3390/agriculture13061221 - 09 Jun 2023

Cited by 1 | Viewed by 622

Abstract

This study focuses on the influence of pre-planting irradiation on the development, health, and yield of seed potatoes infected with Rhizoctonia solani. The research was prompted by the need to ensure crop security and sustainability in the modern-day environment, which calls into question the future sufficiency of crop yields. Considering that the focus has shifted to non-chemical methods of crop treatment at all plant development stages in response to more stringent regulations governing potato production, it is particularly important to refine physics-based methods to suppress fungal diseases caused by Rhizoctonia solani. Irradiation of tubers with 20–150 Gy inhibited the potato development phases and the doses exceeding 150 Gy completely suppressed the potato sprouting. Doses ranging from 20 Gy to 100 Gy decreased the quantity of large tubers by 10–20% on average while the number of medium and small tubers increased by 5–15% and 3–10%, respectively. Irradiation of seed potatoes also decreased the sclerotia and non-sclerotia forms of diseases caused by Rhizoctonia solani in the harvested tubers. It was found that 1 MeV electron irradiation with doses ranging from 20 Gy to 30 Gy is the most efficient for the pre-planting treatment of seed potatoes since the penetration of low-energy accelerated electrons into the upper layers of potato tubers ensures the suppression of diseases caused by Rhizoctonia solani by at least 10% from the value of non-irradiated samples and prevents the reduction of total yield allowing for a maximum of 25% loss. Full article

(This article belongs to the Special Issue Innovative Methods and Technology for Resilience Agrifood Systems)

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Open AccessArticle

Application of Computational Intelligence in Describing Dust Emissions in Different Soil Tillage Applications in Middle Anatolia

and

Nicolae-Valentin Vlăduț

Agriculture 2023, 13(5), 1011; https://doi.org/10.3390/agriculture13051011 - 04 May 2023

Viewed by 727

Abstract

Soil degradation is an increasing problem in Turkey, especially in the Middle Anatolia region where the annual precipitation is approximately 300 mm, resulting from conventional farming methods. To address this issue, the artificial neural networks (ANNs) are used, as they are flexible mathematical tools that capture data. This study aims to investigate the relationships between dust emission (PM₁₀) and the mean weight diameter, shear stress, and stubble amount of the soil, which were measured in eight different tillage practices (conventional tillage, six types of reduced tillage, and direct seeding). The results show that the mean weight diameter, shear stress, and stubble amount of the soil varied between 4.89 and 14.17 mm, 0.40–1.23 N·cm⁻², and 30.5–158 g·m⁻², respectively, depending on the type of tillage works. Additionally, dust emissions generated during different tillage applications ranged from 27.73 to 153.45 mg·m⁻³. The horizontal shaft rototiller produced the highest dust emission, approximately 150% higher than those of disc harrow and winged chisel plows. The impact of tillage practices on dust emission was statistically significant (p < 0.01). A sophisticated 3-(7-7)-1 ANNs model using a backpropagation learning algorithm was developed to predict the concentration of dust, which outperformed the traditional statistical models. The model was based on the values of mean weight diameter, shear stress, and stubble amount of the soil after tillage. The best result was obtained from the ANN model among the polynomial and ANN models. In the ANN model, the coefficient of determination, root mean square error, and mean error were found to be 0.98, 6.70, and 6.11%, respectively. This study demonstrated the effectiveness of ANNs in predicting the levels of dust concentration based on soil tillage data, and it highlighted the importance of adopting alternative tillage practices to reduce soil degradation and dust emissions. Full article

(This article belongs to the Special Issue Innovative Methods and Technology for Resilience Agrifood Systems)

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Open AccessEditor’s ChoiceArticle

Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning

and

Agriculture 2023, 13(4), 839; https://doi.org/10.3390/agriculture13040839 - 08 Apr 2023

Cited by 1 | Viewed by 882

Abstract

Codling moth (CM) is a major apple pest. Current manual method of detection is not very effective. The development of nondestructive monitoring and detection methods has the potential to reduce postharvest losses from CM infestation. Previous work from our group demonstrated the effectiveness of hyperspectral imaging (HSI) and acoustic methods as suitable techniques for nondestructive CM infestation detection and classification in apples. However, both have limitations that can be addressed by the strengths of the other. For example, acoustic methods are incapable of detecting external CM symptoms but can determine internal pest activities and morphological damage, whereas HSI is only capable of detecting the changes and damage to apple surfaces and up to a few mm inward; it cannot detect live CM activity in apples. This study investigated the possibility of sensor data fusion from HSI and acoustic signals to improve the detection of CM infestation in apples. The time and frequency domain acoustic features were combined with the spectral features obtained from the HSI, and various classification models were applied. The results showed that sensor data fusion using selected combined features (mid-level) from the sensor data and three apple varieties gave a high classification rate in terms of performance and reduced the model complexity with an accuracy up to 94% using the AdaBoost classifier, when only six acoustic and six HSI features were applied. This result affirms that the sensor fusion technique can improve CM infestation detection in pome fruits such as apples. Full article

(This article belongs to the Special Issue Innovative Methods and Technology for Resilience Agrifood Systems)

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Open AccessArticle

Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks

Alaa Saeed

A. A. Abdel-Aziz

Amr Mossad

Mahmoud A. Abdelhamid

Alfadhl Y. Alkhaled

and

Muhammad Mayhoub

Agriculture 2023, 13(1), 139; https://doi.org/10.3390/agriculture13010139 - 05 Jan 2023

Cited by 4 | Viewed by 2103

Abstract

Plant diseases affect the availability and safety of plants for human and animal consumption and threaten food safety, thus reducing food availability and access, as well as reducing crop yield and quality. There is a need for novel disease detection methods that can be used to reduce plant losses due to disease. Thus, this study aims to diagnose tomato leaf diseases by classifying healthy and unhealthy tomato leaf images using two pre-trained convolutional neural networks (CNNs): Inception V3 and Inception ResNet V2. The two models were trained using an open-source database (PlantVillage) and field-recorded images with a total of 5225 images. The models were investigated with dropout rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, and 50%. The most important results showed that the Inception V3 model with a 50% dropout rate and the Inception ResNet V2 model with a 15% dropout rate, as they gave the best performance with an accuracy of 99.22% and a loss of 0.03. The high-performance rate shows the possibility of utilizing CNNs models for diagnosing tomato diseases under field and laboratory conditions. It is also an approach that can be expanded to support an integrated system for diagnosing various plant diseases. Full article

(This article belongs to the Special Issue Innovative Methods and Technology for Resilience Agrifood Systems)

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Open AccessArticle

Application of Machine Learning to Study the Agricultural Mechanization of Wheat Farms in Egypt

Hassan A. A. Sayed

Qishuo Ding

Mahmoud A. Abdelhamid

Joseph O. Alele

Alfadhl Y. Alkhaled

and

Mohamed Refai

Agriculture 2023, 13(1), 70; https://doi.org/10.3390/agriculture13010070 - 26 Dec 2022

Cited by 2 | Viewed by 1878

Abstract

Agricultural production can achieve sustainability by appropriately applying agricultural mechanization, especially in developing countries where smallholding farmers lack sufficient agricultural machinery for their farming operations. This paper aimed to study the extent to which small-, medium-, and large-scale farms in the Delta of Egypt use agricultural mechanization in their wheat crop farming operations. K-means clustering was used to aggregate and analyze the scenarios implemented by farmers for wheat cultivation so as to suggest guidelines for each cluster of farmers on how to mechanize their indoor wheat agricultural operations to maximize production. The study is divided into two parts: Firstly, data were collected regarding the percentage of small, medium, and large farms; the cultivated area of wheat crops in small-, medium-, and large-scale farms; and the size of tractors, as an indicator of the mechanization available in the governorates of Egypt’s Delta. Secondly, data were collected through a questionnaire survey of 2652 smallholding farmers, 328 medium-holding farmers, and 354 large-holding farmers from Egypt’s Delta governorates. Based on the surveyed data, 14, 14, and 12 scenarios (indexes) were established for small-, medium-, and large-scale farms, respectively, related to various agricultural operations involved in wheat crop production. These scenarios were analyzed based on the centroids using K-means clustering. The identified scenarios were divided into three clusters for the three levels of farms. The data obtained showed the need for smallholding farmers to implement mechanization, which could be achieved through renting services. These findings, if implemented, would have huge social and economic effects on farmers’ lives, in addition to increasing production, saving time and effort, and reducing dependence on labor. Full article

(This article belongs to the Special Issue Innovative Methods and Technology for Resilience Agrifood Systems)

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