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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: 20 May 2024 | Viewed by 12315

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Special Issue Editors

Dr. Yang Li

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Guest Editor

College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Interests: smart agriculture; machine learning; data analysis

Prof. Dr. Jiachen Yang

E-Mail Website
Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300000, China
Interests: smart ocean system; intelligent monitoring; sensing network; Internet of Things; marine information processing; vision sensors
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Dear Colleagues,

Digital agriculture has developed rapidly and significantly improved productivity in recent years. Information and communication technologies’ application in various agricultural production processes has further improved their level of intelligence. However, chemical pesticides, plastic film residues, irrigation water waste and other problems in traditional agricultural production still present serious setbacks in different countries and regions. These environmental problems may result in long-term effects and serious health implications.

Smart agricultural research driven by artificial intelligence could solve these problems and aid the pursuit of realizing green agriculture for sustainable development, particularly the early and timely identification and monitoring of crop pests and diseases, preventing major disasters; soil moisture monitoring and weather prediction, informing irrigation for water saving; and unmanned harvesting machines, which coordinate operations to increase harvest efficiency and reduce fuel consumption and carbon emissions, among others.

This Special Issue will publish both original articles and reviews considering intelligent applications in agriculture. Papers covering artificial intelligence techniques advancing sustainable development are welcome.

Dr. Yang Li
Prof. Dr. Jiachen Yang
Guest Editors

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Keywords

crop pest identification
yield forecasting
path planning
intelligent irrigation
land use
sustainable fertilizer
sustainable postharvest
plant health monitoring
soil quality
renewable energy

Published Papers (6 papers)

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Research

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18 pages, 2399 KiB

Open AccessArticle

Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India

by Anurag Satpathi, Parul Setiya, Bappa Das, Ajeet Singh Nain, Prakash Kumar Jha, Surendra Singh and Shikha Singh

Sustainability 2023, 15(3), 2786; https://doi.org/10.3390/su15032786 - 03 Feb 2023

Cited by 16 | Viewed by 2900

Abstract

Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur (

R_{c a l}^{2}

= 1,

R_{v a l}^{2}

= 1 and

R M S E_{c a l}

= 0.002,

R M S E_{v a l}

= 0.003) and Surguja (

R_{c a l}^{2}

= 1,

R_{v a l}^{2}

= 0.99 and

R M S E_{c a l}

= 0.004,

R M S E_{v a l}

= 0.214) districts as compared to the other models, whereas for Bastar, ELNET (

R_{c a l}^{2}

= 90,

R_{v a l}^{2}

= 0.48) and LASSO (

R_{c a l}^{2}

= 93,

R_{v a l}^{2}

= 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R² = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Green Agriculture for Sustainable Development)

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19 pages, 2887 KiB

Open AccessArticle

Playing Behavior Classification of Group-Housed Pigs Using a Deep CNN-LSTM Network

by Beng Ern Low, Yesung Cho, Bumho Lee and Mun Yong Yi

Sustainability 2022, 14(23), 16181; https://doi.org/10.3390/su142316181 - 04 Dec 2022

Cited by 1 | Viewed by 1592

Abstract

The swine industry is one of the industries that progressively incorporates smart livestock farming (SLF) to monitor the grouped-housed pigs’ welfare. In recent years, pigs’ positive welfare has gained much attention. One of the evident behavioral indicators of positive welfare is playing behaviors. However, playing behavior is spontaneous and temporary, which makes the detection of playing behaviors difficult. The most direct method to monitor the pigs’ behaviors is a video surveillance system, for which no comprehensive classification framework exists. In this work, we develop a comprehensive pig playing behavior classification framework and build a new video-based classification model of pig playing behaviors using deep learning. We base our deep learning framework on an end-to-end trainable CNN-LSTM network, with ResNet34 as the CNN backbone model. With its high classification accuracy of over 92% and superior performances over the existing models, our proposed model highlights the importance of applying the global maximum pooling method on the CNN final layer’s feature map and leveraging a temporal attention layer as an input to the fully connected layer for final prediction. Our work has direct implications on advancing the welfare assessment of group-housed pigs and the current practice of SLF. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Green Agriculture for Sustainable Development)

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13 pages, 3287 KiB

Open AccessArticle

Image Information Contribution Evaluation for Plant Diseases Classification via Inter-Class Similarity

by Jiachen Yang, Yue Yang, Yang Li, Shuai Xiao and Sezai Ercisli

Sustainability 2022, 14(17), 10938; https://doi.org/10.3390/su141710938 - 01 Sep 2022

Cited by 4 | Viewed by 1516

Abstract

Combineingplant diseases identification and deep learning algorithm can achieve cost-effective prevention effect, and has been widely used. However, the current field of intelligent plant diseases identification still faces the problems of insufficient data and inaccurate classification. Aiming to resolve these problems, the present research proposes an image information contribution evaluation method based on the analysis of inter-class similarity. Combining this method with the active learning image selection strategy can provide guidance for the collection and annotation of intelligent identification datasets of plant diseases, so as to improve the recognition effect and reduce the cost. The method proposed includes two modules: the inter-classes similarity evaluation module and the image information contribution evaluation module. The images located on the decision boundary between high similarity classes will be analysis as high information contribution images, they will provide more information for plant diseases classification. In order to verify the effectiveness of this method, experiments were carried on the fine-grained classification dataset of tomato diseases. Experimental results confirm the superiority of this method compared with others. This research is in the field of plant disease classification. For the detection and segmentation, further research is advisable. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Green Agriculture for Sustainable Development)

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21 pages, 13790 KiB

Open AccessArticle

Comprehensive Analysis of Grain Production Based on Three-Stage Super-SBM DEA and Machine Learning in Hexi Corridor, China

by Zhengxiao Yan, Wei Zhou, Yuyi Wang and Xi Chen

Sustainability 2022, 14(14), 8881; https://doi.org/10.3390/su14148881 - 20 Jul 2022

Cited by 9 | Viewed by 1855

Abstract

Food security is always a pressing agenda worldwide. The grain production in many areas has decreased due to the reduction in agricultural research funding and infrastructure investment. In this paper, we employed the Extreme-Tree algorithm to determine the main effectors in grain production in Hexi Corridor, Gansu, China, during 2002–2018. First, we applied the three-stage super-SBM DEA to precisely assess agricultural production. Then, we used the Extremely randomized trees algorithm to quantify the importance of each factor. Our results show that the variant of average efficiency score at the first stage was minimal. After removing the influence of environmental factors on production efficiency, the more accurate efficiency score was decreasing from 2002 to 2018. The R² value of the Extra-Tree model was 0.989 in the grain production analysis. Our research shows that grain production in the Hexi Corridor was controlled by human-driven but not nature-driven during our research period. Based on the importance attribution analysis of each model, it showed that the importance of human-driven investment occupied 93.7% of grain production. The importance of nature-driving was about 6.3%. Accordingly, we proposed corresponding opinions and suggestions to government and growers. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Green Agriculture for Sustainable Development)

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11 pages, 757 KiB

Open AccessArticle

Efficient Data-Driven Crop Pest Identification Based on Edge Distance-Entropy for Sustainable Agriculture

by Jiachen Yang, Shukun Ma, Yang Li and Zhuo Zhang

Sustainability 2022, 14(13), 7825; https://doi.org/10.3390/su14137825 - 27 Jun 2022

Cited by 15 | Viewed by 1797

Abstract

Human agricultural activities are always accompanied by pests and diseases, which have brought great losses to the production of crops. Intelligent algorithms based on deep learning have achieved some achievements in the field of pest control, but relying on a large amount of data to drive consumes a lot of resources, which is not conducive to the sustainable development of smart agriculture. The research in this paper starts with data, and is committed to finding efficient data, solving the data dilemma, and helping sustainable agricultural development. Starting from the data, this paper proposed an Edge Distance-Entropy data evaluation method, which can be used to obtain efficient crop pests, and the data consumption is reduced by 5% to 15% compared with the existing methods. The experimental results demonstrate that this method can obtain efficient crop pest data, and only use about 60% of the data to achieve 100% effect. Compared with other data evaluation methods, the method proposed in this paper achieve state-of-the-art results. The work conducted in this paper solves the dilemma of the existing intelligent algorithms for pest control relying on a large amount of data, and has important practical significance for realizing the sustainable development of modern smart agriculture. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Green Agriculture for Sustainable Development)

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Review

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23 pages, 2276 KiB

Open AccessReview

Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

by Yiyuan Pang, Francesco Marinello, Pan Tang, Hong Li and Qi Liang

Sustainability 2023, 15(23), 16420; https://doi.org/10.3390/su152316420 - 29 Nov 2023

Cited by 2 | Viewed by 1346

Abstract

Agriculture is considered one of the most critical sectors that play a strategic role in ensuring food security. It is directly related to human development and social stability. The agricultural sector is currently incorporating new technologies from other areas. These phenomena are smart agriculture and smart irrigation. However, a challenge to research is the integration of technologies from different knowledge fields, which has caused theoretical and practical difficulties. Thus, our purpose in this study has been to understand the core of these two themes. We extracted publications in Scopus and used bibliometric methods for high-frequency word and phrase analysis. Research shows that current research on smart agriculture mainly focuses on the Internet of Things, climate change, machine learning, precision agriculture and wireless sensor networks. Simultaneously, the Internet of Things, irrigation systems, soil moisture, wireless sensor networks and climate change have received the most scholarly attention in smart irrigation. This study used cluster analysis to find that the IoT has the most apparent growth rate in smart agriculture and smart irrigation, with five-year growth rates of 1617% and 2285%, respectively. In addition, machine learning, deep learning and neural networks have enormous potential in smart irrigation compared with smart agriculture. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Green Agriculture for Sustainable Development)

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