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Digital Technology for Smart Agriculture: Applications, Challenges, and Outlooks

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (21 November 2023) | Viewed by 6825

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Special Issue Editors

Prof. Dr. Shih-Chang Huang

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Guest Editor

Department of Computer Science and Information Engineering, National Formosa University, Huwei Township 632, Yunlin Country, Taiwan
Interests: wireless sensor networks; Internet of Things; intelligent agriculture systems; intelligent systems

Dr. Ming-Shen Jian

E-Mail Website
Guest Editor

Department of Computer Science and Information Engineering, National Formosa University, Huwei Township 632, Yunlin Country, Taiwan
Interests: open-source cloud computing; artificial intelligence; big data; AioT

Special Issue Information

Dear Colleagues,

Extreme weather poses a significant challenge to traditional agriculture, affecting the quality and quantity of planted crops. To ensure a stable crop yield and reduce planting costs, creating a suitable planting environment and optimizing the invested resources, such as water, fertilizer, and the cultivated area, are crucial. Digital technology can be implemented in traditional agriculture to achieve smart agriculture practices. Smart agriculture uses the IoT and wireless communication to monitor and collect information on cultivated land, applies big data analysis to predict or forecast future outcomes, and employs robots to save on labor. The final goal of this process is to achieve sustainable agriculture, precision agriculture, and unmanned agriculture.

This Special Issue, “Digital Technology for Smart Agriculture: Applications, Challenges, and Outlooks”, welcomes high-quality studies focused on pioneering technologies involving the Internet of Things (IoT), wireless communication, cloud computing, big data, machine learning, artificial intelligence, etc., and their applications in smart agriculture.

Prof. Dr. Shih-Chang Huang
Dr. Ming-Shen Jian
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. Sustainability 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 2400 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 agriculture
digital technology
sustainable agriculture
precision agriculture
unmanned agriculture

Published Papers (5 papers)

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Research

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14 pages, 485 KiB

Open AccessArticle

Kano Model Analysis of Digital On-Farm Technologies for Climate Adaptation and Mitigation in Livestock Farming

by Pia Münster and Barbara Grabkowsky

Sustainability 2024, 16(1), 268; https://doi.org/10.3390/su16010268 - 27 Dec 2023

Viewed by 640

Abstract

In the EU, agriculture contributes significantly to greenhouse gas (GHG) emissions. In Germany, over half of the GHG emissions from agriculture can be directly attributed to livestock farming. To combat the progressing climate change, GHG emissions must be significantly reduced. Digital solutions, particularly decision support systems (DSS), are promising tools to assist livestock farmers in achieving the globally agreed GHG reduction goals. However, there is a lack of studies addressing DSS requirements for reducing GHG emissions in livestock on the farm level. Users’ feedback on technologies can support identifying areas for enhancement and refinement. This study identifies, categorizes, and ranks fourteen DSS features aimed at supporting GHG reduction based on their impact on customer satisfaction. A quantitative online questionnaire using the Kano model surveyed livestock farmers’ satisfaction or dissatisfaction levels with these features. Results gathered from 98 responses across German federal states highlighted the significance of data authority and integrability, with their absence causing dissatisfaction. Multi-target optimization emerged as an attractive feature, positively impacting satisfaction. Connectivity and market perspective, however, appeared indifferent. The findings guide DSS developers in prioritizing attributes crucial for customer satisfaction. It also helps to focus on must-have attributes to preserve customer satisfaction and ensure successful GHG reduction implementation. Full article

(This article belongs to the Special Issue Digital Technology for Smart Agriculture: Applications, Challenges, and Outlooks)

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14 pages, 3693 KiB

Open AccessArticle

A Model for Yield Estimation Based on Sea Buckthorn Images

by Yingjie Du, Haichao Wang, Chunguang Wang, Chunhui Zhang and Zheying Zong

Sustainability 2023, 15(14), 10872; https://doi.org/10.3390/su151410872 - 11 Jul 2023

Cited by 1 | Viewed by 720

Abstract

Sea buckthorn is an extremely drought-tolerant, resilient and sustainable crop that can be grown in areas with harsh climates and scarce resources to provide a source of nutrition and income for the local population. The use of image-based yield estimation methods allows for better management of sea buckthorn cultivation to improve its productivity and sustainability, while the error in fruit yield information due to occlusion can be well reduced by combining and analysing the image features extracted using binocular cameras. In this paper, mature wild sea buckthorn in the mountainous areas north of Hohhot City, Inner Mongolia Autonomous Region, were used as the study target. Firstly, complete images of sea buckthorn branches were collected by binocular cameras and features were extracted. The extracted features include the colour index of sea buckthorn fruits, the number of fruits and a total of four texture parameters, ASM, CON, COR and HOM. The features with significant correlation to sea buckthorn fruit weight were selected by correlation calculation of the feature parameters, the obtained correlation features were introduced into the BP neural network model for training and then the sea buckthorn estimation model was obtained. The results showed that the best yield estimation model was achieved by combining the COR index with the colour index and the number of sea buckthorn fruits, with a coefficient of determination R² = 0.99267 and a root mean square error RMSE = 0.5214. Full article

(This article belongs to the Special Issue Digital Technology for Smart Agriculture: Applications, Challenges, and Outlooks)

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17 pages, 4707 KiB

Open AccessArticle

Deep-Learning-Based Strawberry Leaf Pest Classification for Sustainable Smart Farms

by Haram Kim and Dongsoo Kim

Sustainability 2023, 15(10), 7931; https://doi.org/10.3390/su15107931 - 12 May 2023

Cited by 2 | Viewed by 1574

Abstract

This paper presents a deep-learning-based classification model that aims to detect diverse pest infections in strawberry plants. The proposed model enables the timely identification of pest symptoms, allowing for prompt and effective pest management in smart farms. The present research employed an actual dataset of strawberry leaf images collected from a smart farm test bed. To expand the dataset, open data from sources such as Kaggle were utilized, while diseased leaf images were obtained through web crawling with the aid of the Python library. Subsequently, the expanded and added data were resized to a uniform size, and Pseudo-Labeling was implemented to ensure stable learning for both the training and test datasets. The RegNet and EfficientNet models were selected as the primary CNN-based image network models for repetitive learning, and ensemble learning was employed to enhance prediction accuracy. The proposed model is anticipated to facilitate the early identification and treatment of pests on strawberry leaves during the seedling period, a pivotal phase in smart farm development. Furthermore, it is expected to boost production in the agricultural industry and strengthen its competitive edge. Full article

(This article belongs to the Special Issue Digital Technology for Smart Agriculture: Applications, Challenges, and Outlooks)

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15 pages, 2610 KiB

Open AccessArticle

Comprehensive Evaluation and Promotion Strategy of Agricultural Digitalization Level

by Min Zhu, Yajie Li, Zainab Khalid and Ehsan Elahi

Sustainability 2023, 15(8), 6528; https://doi.org/10.3390/su15086528 - 12 Apr 2023

Cited by 2 | Viewed by 1252

Abstract

The development of digitalization is a crucial aspect of agricultural progress, and expediting the establishment of digital systems is a significant driving force behind high-quality agricultural advancements in the current era. Utilizing data from 16 cities within Shandong Province in China between 2014 and 2020, we created an assessment system to measure the degree of agricultural digitalization, utilized the entropy technique to assess the level of digitalization, scrutinized the general trends and time-dependent features of each city, and then utilized the obstacle degree model to pinpoint the primary hindrances to digitalization in agriculture. Lastly, the ESDA method was utilized to examine the differences in spatial distribution among regions and the spatial characteristics of agricultural digitalization at different stages and levels. Overall, the degree of agricultural digitalization can be categorized into three stages: deceleration and upswing (2014–2015), steady fluctuation (2016–2017), and high-level upswing (2018–2020). From the perspective of obstacles, the main hurdles to agricultural digitalization are e-commerce transaction volume and the total amount of telecommunication business. To accelerate the development of the entire agricultural industry chain, it is required to leverage the strengths of high-value areas and reinforce the coordination mechanism among various departments while hastening the construction of rural infrastructure in low-value areas. Additionally, it is necessary to improve inter-regional communication and cooperation to nurture different regional development models in line with local conditions. Full article

(This article belongs to the Special Issue Digital Technology for Smart Agriculture: Applications, Challenges, and Outlooks)

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Review

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14 pages, 1558 KiB

Open AccessReview

Factors Affecting the Adoption of Digital Technology by Farmers in China: A Systematic Literature Review

by Luwen Cui and Weiwei Wang

Sustainability 2023, 15(20), 14824; https://doi.org/10.3390/su152014824 - 12 Oct 2023

Viewed by 2121

Abstract

Increasing pressure for food security and environmental sustainability has highlighted the need to switch from conventional agricultural methods to advanced agricultural practices. Digital agricultural technologies are considered promising solutions for sustainable intensification of food production and environmental protection. Despite significant promotional efforts initiated in recent years in China, the adoption rate remains low. The objective of this study is to gain insight into the factors affecting the adoption of on-farm digital technologies in China using a systematic review approach that analyzes 10 relevant studies. Data regarding methodological aspects and results are extracted. We identify 19 key adoption drivers that are related to socioeconomic, agroecological, technological, institutional, psychological, and behavioral factors. There is a predominance of ex-ante studies that use stated preference methods. We conclude with a discussion of the design of policy incentives to induce the adoption of digital technologies. Additionally, the review points to the limitations of existing research and suggests approaches that can be adopted for future investigations. This review provides meaningful implications for the development of future efforts to promote digital transformation for sustainable agriculture in China. Full article

(This article belongs to the Special Issue Digital Technology for Smart Agriculture: Applications, Challenges, and Outlooks)

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