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Agriculture 4.0: From Precision Agriculture to Smart Farming
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Agriculture 4.0: From Precision Agriculture to Smart Farming

A topical collection in Applied Sciences (ISSN 2076-3417). This collection belongs to the section "Agricultural Science and Technology".

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Editors

Prof. Dr. Pasquale Catalano

E-Mail Website
Guest Editor
Department of Agricultural, Environmental and Food Sciences, University of Molise, 86100 Campobasso CB, Italy
Interests: sustainable development of smart agriculture; remote sensing (processing of satellite images and/or from drones; IoT and data analysis, etc.) for precision agriculture and industry 4.0 in the whole food production chain; food processing plant automation and optimization; energy saving and natural resources optimization
Prof. Dr. Antonia Tamborrino

E-Mail Website
Guest Editor
Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy
Interests: deals with the innovation and optimization of agro-food industry equipment and plants, design of the food pilot plants and their implementation in the industrial environmental; sensors and real time process control; processes settings; influence of the machine design parameters on food quality; other scientific targets are the waste management; composting systems, harvesting mechanization system and harvesting machine
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Topical Collection Information

Dear Colleagues,

Agriculture 4.0, the natural evolution of precision agriculture, makes it possible to face these challenges by allowing an intelligent and controlled application of inputs (water, nutrients, pesticides, energy), optimized management of production from field to table, integration of different technologies in agriculture, etc. The use of software, applications, networks of sensors, all supported by Internet of Things (IoT) technologies, make data easily manageable and accessible. This allows to accurately provide and analyse information in real time, allowing the automation of agricultural production and decision-making processes. For example, at the current state of knowledge, typical consolidated technologies of precision agriculture allow farmers to program the variable distribution of inputs according to the spatial and temporal variability of crops. On the other hand, the possibilities offered by the latest generation technologies allow for an integrated communication between all the tools present in the farm with a high level data accessibility between the components of the supply chain: machines, equipment, farms, customers, dealers and institutions, etc. Therefore, the agriculture in the future will increasingly use sophisticated technologies such as robots, field sensors, aerial imagery, GPS technology, software completely interconnected by IoT networks allowing farms to be more profitable, efficient, safer and more environmentally friendly. Among the topics we highlight:

History of Precision Agriculture, Sensing Technology for Precision Farming (satellite, aerial, UAV, proximal sensing platforms, etc.), Data Processing and Utilization in Precision Agriculture, Image Processing, Control of Precision Agriculture Production, Big data analysis applied to precision agriculture, Intelligent Agricultural Machinery and Field Robots, Traceability Smart Agriculture, Precision Farming Economics.

Prof. Dr. Pasquale Catalano
Prof. Dr. Antonia Tamborrino
Guest Editors

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Keywords

  • history of precision agriculture
  • sensing technology for precision farming (satellite, aerial, UAV, proximal sensing platforms, etc.)
  • data processing and utilization in precision agriculture
  • image processing
  • control of precision agriculture production
  • big data analysis applied to precision agriculture
  • intelligent agricultural machinery and field robots
  • traceability smart agriculture
  • precision farming economics
  • vegetable and crop yields

Published Papers (8 papers)

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2023

Jump to: 2022, 2021

20 pages, 2414 KiB  
Review
Assessment of Smart Mechatronics Applications in Agriculture: A Review
by Sairoel Amertet, Girma Gebresenbet, Hassan Mohammed Alwan and Kochneva Olga Vladmirovna
Appl. Sci. 2023, 13(12), 7315; https://0-doi-org.brum.beds.ac.uk/10.3390/app13127315 - 20 Jun 2023
Cited by 1 | Viewed by 2848
Abstract
Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. The [...] Read more.
Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. The aim of this study was to review smart mechatronics applications introduced in agriculture to date, and the different areas of the sector in which they are being employed. Various literature search approaches were used to obtain an overview of the current state-of-the-art, benefits, and drawbacks of smart mechatronics systems. Smart mechatronics modules and various networks applied in the processing of agricultural products were examined. Finally, relationships in the data retrieved were tested using a one-way analysis of variance on keywords and sources. The review revealed limited use of sophisticated mechatronics in the agricultural industry in practice at a time of falling production rates and a dramatic decline in the reliability of the global food supply. Smart mechatronics systems could be used in different agricultural enterprises to overcome these issues. Full article
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2022

Jump to: 2023, 2021

20 pages, 6824 KiB  
Article
Intelligent Data Analytics Framework for Precision Farming Using IoT and Regressor Machine Learning Algorithms
by Ashay Rokade, Manwinder Singh, Praveen Kumar Malik, Rajesh Singh and Turki Alsuwian
Appl. Sci. 2022, 12(19), 9992; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199992 - 05 Oct 2022
Cited by 23 | Viewed by 2861
Abstract
Smart farming with precise greenhouse monitoring in various scenarios is vital for improved agricultural growth management. The Internet of Things (IoT) leads to a modern age in computer networking that is gaining traction. This paper used a regression-based supervised machine learning approach to [...] Read more.
Smart farming with precise greenhouse monitoring in various scenarios is vital for improved agricultural growth management. The Internet of Things (IoT) leads to a modern age in computer networking that is gaining traction. This paper used a regression-based supervised machine learning approach to demonstrate a precise control of sensing parameters, CO2, soil moisture, temperature, humidity, and light intensity, in a smart greenhouse agricultural system. The proposed scheme comprised four main components: cloud, fog, edge, and sensor. It was found that the greenhouse could be remotely operated for the control of CO2, soil moisture, temperature, humidity, and light, resulting in improved management. Overall implementation was remotely monitored via the IoT using Message Query Telemetry Transport (MQTT), and sensor data were analysed for their standard and anomalous behaviours. Then, for practical computation over the cloud layer, an analytics and decision-making system was developed in the fog layer and constructed using supervised machine learning algorithms for precise management using regression modelling methods. The proposed framework improved its presentation and allowed us to properly accomplish the goal of the entire framework. Full article
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33 pages, 15720 KiB  
Article
Semantic Segmentation of Agricultural Images Based on Style Transfer Using Conditional and Unconditional Generative Adversarial Networks
by Hirokazu Madokoro, Kota Takahashi, Satoshi Yamamoto, Stephanie Nix, Shun Chiyonobu, Kazuki Saruta, Takashi K. Saito, Yo Nishimura and Kazuhito Sato
Appl. Sci. 2022, 12(15), 7785; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157785 - 02 Aug 2022
Cited by 1 | Viewed by 3189
Abstract
Classification, segmentation, and recognition techniques based on deep-learning algorithms are used for smart farming. It is an important and challenging task to reduce the time, burden, and cost of annotation procedures for collected datasets from fields and crops that are changing in a [...] Read more.
Classification, segmentation, and recognition techniques based on deep-learning algorithms are used for smart farming. It is an important and challenging task to reduce the time, burden, and cost of annotation procedures for collected datasets from fields and crops that are changing in a wide variety of ways according to growing, weather patterns, and seasons. This study was conducted to generate crop image datasets for semantic segmentation based on an image style transfer using generative adversarial networks (GANs). To assess data-augmented performance and calculation burdens, our proposed framework comprises contrastive unpaired translation (CUT) for a conditional GAN, pix2pixHD for an unconditional GAN, and DeepLabV3+ for semantic segmentation. Using these networks, the proposed framework provides not only image generation for data augmentation, but also automatic labeling based on distinctive feature learning among domains. The Fréchet inception distance (FID) and mean intersection over union (mIoU) were used, respectively, as evaluation metrics for GANs and semantic segmentation. We used a public benchmark dataset and two original benchmark datasets to evaluate our framework of four image-augmentation types compared with the baseline without using GANs. The experimentally obtained results showed the efficacy of using augmented images, which we evaluated using FID and mIoU. The mIoU scores for the public benchmark dataset improved by 0.03 for the training subset, while remaining similar on the test subset. For the first original benchmark dataset, the mIoU scores improved by 0.01 for the test subset, while they dropped by 0.03 for the training subset. Finally, the mIoU scores for the second original benchmark dataset improved by 0.18 for the training subset and 0.03 for the test subset. Full article
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19 pages, 7949 KiB  
Article
Fuzzy Neural Network PID Strategy Based on PSO Optimization for pH Control of Water and Fertilizer Integration
by Runmeng Zhou, Lixin Zhang, Changxin Fu, Huan Wang, Zihao Meng, Chanchan Du, Yongchao Shan and Haoran Bu
Appl. Sci. 2022, 12(15), 7383; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157383 - 22 Jul 2022
Cited by 4 | Viewed by 1413
Abstract
In the process of crop cultivation, the application of a fertilizer solution with appropriate pH value is more conducive to the absorption of nutrients by crops. If the pH of the irrigation water and fertilizer solution is too high, it will not only [...] Read more.
In the process of crop cultivation, the application of a fertilizer solution with appropriate pH value is more conducive to the absorption of nutrients by crops. If the pH of the irrigation water and fertilizer solution is too high, it will not only be detrimental to the absorption of nutrients by the crop, but will also damage the structure of the soil. Therefore, the precise regulation of pH in water and fertilizer solutions is very important for agricultural production and saving water and fertilizer. Firstly, the article investigates the hybrid control of fertilizer and water conditioning systems, then builds a fuzzy preprocessing controller and a neural network proportional–integral–differential controller, and optimizes the neural network parameters by means of an improved particle swarm algorithm. The effectiveness of the controller was verified by comparison with the common proportional–integral–differential control and fuzzy algorithm control for fertilizer control and fuzzy preprocessing neural network control. Simulation experiments for this study were designed through the MATLAB/Simulink simulation platform, and the simulation results show that the algorithm has good tracking and regulation capabilities in the system. Finally, the four control algorithms are experimentally validated under different pH regulations using designed field experiments. The results show that, compared with other control algorithms, the control algorithm in this paper has a smaller overshoot and good stability with a shorter rise time, which can achieve the purpose of better regulating the fertilizer application system. Full article
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17 pages, 35799 KiB  
Article
Spatial Location of Sugarcane Node for Binocular Vision-Based Harvesting Robots Based on Improved YOLOv4
by Changwei Zhu, Chujie Wu, Yanzhou Li, Shanshan Hu and Haibo Gong
Appl. Sci. 2022, 12(6), 3088; https://0-doi-org.brum.beds.ac.uk/10.3390/app12063088 - 17 Mar 2022
Cited by 9 | Viewed by 1986
Abstract
Spatial location of sugarcane nodes using robots in agricultural conditions is a challenge in modern precision agriculture owing to the complex form of the sugarcane node when wrapped with leaves and the high computational demand. To solve these problems, a new binocular location [...] Read more.
Spatial location of sugarcane nodes using robots in agricultural conditions is a challenge in modern precision agriculture owing to the complex form of the sugarcane node when wrapped with leaves and the high computational demand. To solve these problems, a new binocular location method based on the improved YOLOv4 was proposed in this paper. First, the YOLOv4 deep learning algorithm was improved by the Channel Pruning Technology in network slimming, so as to ensure the high recognition accuracy of the deep learning algorithm and to facilitate transplantation to embedded chips. Secondly, the SIFT feature points were optimised by the RANSAC algorithm and epipolar constraint, which greatly reduced the mismatching problem caused by the similarity between stem nodes and sugarcane leaves. Finally, by using the optimised matching point to solve the homography transformation matrix, the space location of the sugarcane nodes was for the first time applied to the embedded chip in the complex field environment. The experimental results showed that the improved YOLOv4 algorithm reduced the model size, parameters and FLOPs by about 89.1%, while the average precision (AP) of stem node identification only dropped by 0.1% (from 94.5% to 94.4%). Compared with other deep learning algorithms, the improved YOLOv4 algorithm also has great advantages. Specifically, the improved algorithm was 1.3% and 0.3% higher than SSD and YOLOv3 in average precision (AP). In terms of parameters, FLOPs and model size, the improved YOLOv4 algorithm was only about 1/3 of SSD and 1/10 of YOLOv3. At the same time, the average locational error of the stem node in the Z direction was only 1.88 mm, which totally meets the demand of sugarcane harvesting robots in the next stage. Full article
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29 pages, 5582 KiB  
Review
A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses
by Muhammet Fatih Aslan, Akif Durdu, Kadir Sabanci, Ewa Ropelewska and Seyfettin Sinan Gültekin
Appl. Sci. 2022, 12(3), 1047; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031047 - 20 Jan 2022
Cited by 92 | Viewed by 8449
Abstract
The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should [...] Read more.
The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse. Full article
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2021

Jump to: 2023, 2022

26 pages, 6827 KiB  
Article
Mallard Detection Using Microphone Arrays Combined with Delay-and-Sum Beamforming for Smart and Remote Rice–Duck Farming
by Hirokazu Madokoro, Satoshi Yamamoto, Kanji Watanabe, Masayuki Nishiguchi, Stephanie Nix, Hanwool Woo and Kazuhito Sato
Appl. Sci. 2022, 12(1), 108; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010108 - 23 Dec 2021
Cited by 1 | Viewed by 1653
Abstract
This paper presents an estimation method for a sound source of pre-recorded mallard calls from acoustic information using two microphone arrays combined with delay-and-sum beamforming. Rice farming using mallards saves labor because mallards work instead of farmers. Nevertheless, the number of mallards declines [...] Read more.
This paper presents an estimation method for a sound source of pre-recorded mallard calls from acoustic information using two microphone arrays combined with delay-and-sum beamforming. Rice farming using mallards saves labor because mallards work instead of farmers. Nevertheless, the number of mallards declines when they are preyed upon by natural enemies such as crows, kites, and weasels. We consider that efficient management can be achieved by locating and identifying the locations of mallards and their natural enemies using acoustic information that can be widely sensed in a paddy field. For this study, we developed a prototype system that comprises two sets of microphone arrays. We used 64 microphones in all installed on our originally designed and assembled sensor mounts. We obtained three acoustic datasets in an outdoor environment for our benchmark evaluation. The experimentally obtained results demonstrated that the proposed system provides adequate accuracy for application to rice–duck farming. Full article
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16 pages, 6209 KiB  
Article
Design of a Sweet Potato Transplanter Based on a Robot Arm
by Zhengduo Liu, Xu Wang, Wenxiu Zheng, Zhaoqin Lv and Wanzhi Zhang
Appl. Sci. 2021, 11(19), 9349; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199349 - 08 Oct 2021
Cited by 6 | Viewed by 2582
Abstract
Traditional sweet potato transplanters have the problem of seedling leakage and can only accomplish one transplantation method at a time, which does not meet the requirements of complex planting terrain that requires multiple transplantation methods. Therefore, this paper proposes a design for a [...] Read more.
Traditional sweet potato transplanters have the problem of seedling leakage and can only accomplish one transplantation method at a time, which does not meet the requirements of complex planting terrain that requires multiple transplantation methods. Therefore, this paper proposes a design for a crawler-type sweet potato transplanting machine, which can accomplish a variety of transplanting trajectories and conduct automatic replanting. The machine has a transplanting piece and a replanting piece. The transplanting piece completes the transplanting action through a transplanting robot arm, and the replanting piece detects the transplanting status by deep learning. The mathematical model of the transplanting robot arm is built, and the transplanting trajectory is inferred from the inverse kinematics model of the transplanting robot. In the replanting piece, a target detection network is used to detect the transplanting status. The DBIFPN structure and the CBAM_Dense attention mechanism are proposed to improve the accuracy of the target detection of sweet potato seedlings. The experiment showed that the transplanting robot arm can transplant sweet potatoes in horizontal and vertical methods, and the highest transplanting qualification rate is 96.8%. Compared with the use of the transplanting piece alone, the leakage rate of the transplanting–replanting mechanism decreased by 5.2%. These results provide a theoretical basis and technical support for the research and development of sweet potato transplanters. Full article
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