Sustainable and Smart Agriculture with Artificial Intelligence Techniques

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 11646

Special Issue Editor


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Guest Editor
Department of Electrical Electronic Engineering, Zonguldak Bülent Ecevit University, Zonguldak 67100, Turkey
Interests: signal processing; data mining; artificial intelligence; optimization; real time image processing

Special Issue Information

Dear Colleagues, 

Today, sustainable and smart agriculture is of strategic importance for countries and policy makers aiming to tackle climate change. Sustainable and smart agriculture is emerging based on digital agriculture, focused on utilizing modern information and communication technologies to increase the quantity and quality of agricultural products while reducing the necessary human workload. The aim of this Special Issue is to collect research focusing on advanced Artificial Intelligence (AI), Internet of Things (IoT), and digital twin algorithms in smart agricultural systems, committed to providing solutions for precision agriculture, plant factories, smart greenhouses, etc. It aims to call for state-of-the-art research in theories, algorithms, models, systems, and applications. Original research and review articles are both welcomed.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Scope of the Special Issue: Artificial intelligence applications in the field of sustainable agriculture and agriculture, innovative approaches to agricultural problems, smart intelligence techniques that can cope with climate change, agricultural robot applications for agricultural crop diseases and efficient crop harvesting.

Dr. Aytaç Altan
Guest Editor

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Keywords

  • sustainable and smart agriculture
  • crop harvest
  • artificial intelligence applications
  • organic farming
  • agricultural meteorology
  • precision agriculture
  • sustainable development of agronomy
  • food systems
  • crop disease and protection
  • agroclimatology and soil science

Published Papers (3 papers)

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Research

26 pages, 12302 KiB  
Article
Nutrient Film Technique-Based Hydroponic Monitoring and Controlling System Using ANFIS
by Vito Vincentdo and Nico Surantha
Electronics 2023, 12(6), 1446; https://doi.org/10.3390/electronics12061446 - 18 Mar 2023
Cited by 4 | Viewed by 3162
Abstract
Most people are now aware of the importance of a healthy lifestyle, including the importance of consuming vegetables. As a result, the demand for vegetables has increased, and so their production needs to be increased. Currently, most plantations use soil as a growing [...] Read more.
Most people are now aware of the importance of a healthy lifestyle, including the importance of consuming vegetables. As a result, the demand for vegetables has increased, and so their production needs to be increased. Currently, most plantations use soil as a growing medium, which is time-consuming and requires a significant amount of space. To modernize cultivation, hydroponic techniques should be adopted. However, implementing hydroponics can be challenging as it requires precise pH and nutrient adjustments. The previous research has proposed the hydroponic pH and nutrient control using the Sugeno fuzzy method. However, in Sugeno fuzzy method, there is no systematic procedure in designing the fuzzy controller, thus, the design relies on hydroponic expert knowledge. To address this issue, a smart hydroponic system was developed using the adaptive neuro-fuzzy inference system (ANFIS) method, which allows for automatic adjustments based on the collected dataset and remote control through internet of things (IoT) technology. This study showed that the system could accurately adjust pH and nutrient levels, allowing plants to grow better. Furthermore, the fuzzy controller created using ANFIS is 67% more accurate than creating the fuzzy controller using the Sugeno fuzzy method. Finally, the web application dashboard of the proposed system is also presented in this paper. Full article
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18 pages, 4682 KiB  
Article
An Improved Vision Transformer Network with a Residual Convolution Block for Bamboo Resource Image Identification
by Qing Zou, Xiu Jin, Yi Song, Lianglong Wang, Shaowen Li, Yuan Rao, Xiaodan Zhang and Qijuan Gao
Electronics 2023, 12(4), 1055; https://doi.org/10.3390/electronics12041055 - 20 Feb 2023
Viewed by 1948
Abstract
Bamboo is an important economic crop with up to a large number of species. The distribution of bamboo species is wide; therefore, it is difficult to collect images and make the recognition model of a bamboo species with few amount of images. In [...] Read more.
Bamboo is an important economic crop with up to a large number of species. The distribution of bamboo species is wide; therefore, it is difficult to collect images and make the recognition model of a bamboo species with few amount of images. In this paper, nineteen species of bamboo with a total of 3220 images are collected and divided into a training dataset, a validation dataset and a test dataset. The main structure of a residual vision transformer algorithm named ReVI is improved by combining the convolution and residual mechanisms with a vision transformer network (ViT). This experiment explores the effect of reducing the amount of bamboo training data on the performance of ReVI and ViT on the bamboo dataset. The ReVI has a better generalization of a deep model with small-scale bamboo training data than ViT. The performances of each bamboo species under the ReVI, ViT, ResNet18, VGG16, Densenet121, Xception were then compared, which showed that ReVI performed the best, with an average accuracy of 90.21%, and the reasons for the poor performance of some species are discussed. It was found that ReVI offered the efficient identification of bamboo species with few images. Therefore, the ReVI algorithm proposed in this manuscript offers the possibility of accurate and intelligent classification and recognition of bamboo resource images. Full article
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15 pages, 2712 KiB  
Article
The Semantic Segmentation of Standing Tree Images Based on the Yolo V7 Deep Learning Algorithm
by Lianjun Cao, Xinyu Zheng and Luming Fang
Electronics 2023, 12(4), 929; https://doi.org/10.3390/electronics12040929 - 13 Feb 2023
Cited by 20 | Viewed by 6012
Abstract
The existence of humans and the preservation of the natural ecological equilibrium depend greatly on trees. The semantic segmentation of trees is very important. It is crucial to learn how to properly and automatically extract a tree’s elements from photographic images. Problems with [...] Read more.
The existence of humans and the preservation of the natural ecological equilibrium depend greatly on trees. The semantic segmentation of trees is very important. It is crucial to learn how to properly and automatically extract a tree’s elements from photographic images. Problems with traditional tree image segmentation include low accuracy, a sluggish learning rate, and a large amount of manual intervention. This research suggests the use of a well-known network segmentation technique based on deep learning called Yolo v7 to successfully accomplish the accurate segmentation of tree images. Due to class imbalance in the dataset, we use the weighted loss function and apply various types of weights to each class to enhance the segmentation of the trees. Additionally, we use an attention method to efficiently gather feature data while reducing the production of irrelevant feature data. According to the experimental findings, the revised model algorithm’s evaluation index outperforms other widely used semantic segmentation techniques. In addition, the detection speed of the Yolo v7 model is much faster than other algorithms and performs well in tree segmentation in a variety of environments, demonstrating the effectiveness of this method in improving the segmentation performance of the model for trees in complex environments and providing a more effective solution to the tree segmentation issue. Full article
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