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Sustainable Technologies for Improving Soil, Crop, and Environment Quality in Changing Climate

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Soil Conservation and Sustainability".

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 4410

Special Issue Editors


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Guest Editor
Institute of Soil and Environmental Sciences, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 46000, Pakistan
Interests: soil microbiology; soil fertility; soil pollution; climate change; biochar research

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Guest Editor
Institute of Soil and Environmental Sciences, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 46000, Pakistan
Interests: soil microbial biomass and biochemical parameters as an indicator of changes in soil ecosystem; role of soil microbial biomass and its associated pool of nutrients in plant nutrition; improving phosphorus use efficiency in soil–plant system through stimulation of soil microbial activity; recycling of organic wastes and nutrients release to crop plants

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Guest Editor
Department of Land and Water Conservation Engineering, Faculty of Agricultural Engineering and Technology, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 46300, Pakistan
Interests: transboundary river basin management; hydrological modelling; remote sensing; precision agriculture
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Guest Editor
Department of Environmental Sciences and Engineering, Faculty of Engineering and Technology, Government College University Faisalabad, Faisalabad 38000, Pakistan
Interests: soil pollution; wastewater treatment; climate change mitigation strategies

Special Issue Information

Dear Colleagues,

In the face of climate change, crop production for food security is the most difficult challenge because agriculture is both the cause and the solution to the climate change problem. Due to the phenomenon of climate change, agriculture and farm produce is vulnerable to the effects of global warming. Furthermore, international research has confirmed that abrupt climate changes endanger not only food security, but also environmental quality and farming's future. Although there is no choice but to adapt to climate change, cooperation in reducing greenhouse gas (GHG) emissions and soil and water pollution from agricultural fields is necessary. This concept has numerous implications for agricultural strategies, farm practices and technologies, soil and water conservation approaches, crop responses to soil and water pollutions, crop development for climate resilience, and contributions to the global economy. In recent decades, significant progress has been achieved in the field of technologies, procedures, and practices for the long-term improvement of soil, crop, and environmental quality, but the effort to generate knowledge to solve the problem pales in comparison to its magnitude. As a result, climate-smart agriculture is critical for agricultural and environmental sustainability. This Special Issue focuses on the development and application of novel technologies for climate change mitigation strategies and sustainable agriculture in order to ensure global food security. Scientists from relevant backgrounds can present their innovative technologies to make them a reality by submitting research work regarding novel approaches for the efficient management of soil and water resources, precision agriculture, crop and environment protection, and other activities that can synergize with one another and with many other activities to transform agriculture and the environment in a changing climate.

Dr. Qaiser Hussain
Prof. Dr. Khalid Saifullah Khan
Prof. Dr. Muhammad Jehanzeb Masud Cheema
Dr. Muhammad Ibrahim
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

  • climate change
  • soil and water resource management
  • precision agriculture
  • plant protection
  • environmental quality
  • innovative technologies

Published Papers (1 paper)

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Research

19 pages, 74937 KiB  
Article
Small Pests Detection in Field Crops Using Deep Learning Object Detection
by Saim Khalid, Hadi Mohsen Oqaibi, Muhammad Aqib and Yaser Hafeez
Sustainability 2023, 15(8), 6815; https://doi.org/10.3390/su15086815 - 18 Apr 2023
Cited by 12 | Viewed by 3666
Abstract
Deep learning algorithms, such as convolutional neural networks (CNNs), have been widely studied and applied in various fields including agriculture. Agriculture is the most important source of food and income in human life. In most countries, the backbone of the economy is based [...] Read more.
Deep learning algorithms, such as convolutional neural networks (CNNs), have been widely studied and applied in various fields including agriculture. Agriculture is the most important source of food and income in human life. In most countries, the backbone of the economy is based on agriculture. Pests are one of the major challenges in crop production worldwide. To reduce the overall production and economic loss from pests, advancement in computer vision and artificial intelligence may lead to early and small pest detection with greater accuracy and speed. In this paper, an approach for early pest detection using deep learning and convolutional neural networks has been presented. Object detection is applied on a dataset with images of thistle caterpillars, red beetles, and citrus psylla. The input dataset contains 9875 images of all the pests under different illumination conditions. State-of-the-art Yolo v3, Yolov3-Tiny, Yolov4, Yolov4-Tiny, Yolov6, and Yolov8 have been adopted in this study for detection. All of these models were selected based on their performance in object detection. The images were annotated in the Yolo format. Yolov8 achieved the highest mAP of 84.7% with an average loss of 0.7939, which is better than the results reported in other works when compared to small pest detection. The Yolov8 model was further integrated in an Android application for real time pest detection. This paper contributes the implementation of novel deep learning models, analytical methodology, and a workflow to detect pests in crops for effective pest management. Full article
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