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Agriculture
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Neural Networks and Image Analysis in Intelligent Agricultural Engineering
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Neural Networks and Image Analysis in Intelligent Agricultural Engineering

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 9986

Special Issue Editor

Dr. Suresh Neethirajan

E-Mail Website
Guest Editor
Department of Animal Science & Aquaculture, Faculty of Agriculture & Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada
Interests: digital agriculture; artificial intelligence; big data analytics; animal-computer interaction; sensors & bio-instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Three decades of systematic farm-to-fork strategy-based ICT (information and communication technology) modelling and evaluations of the efficiency of mitigation measures have led to significant improvements in the addressal of challenges and acceleration in the transition to sustainable food systems. The agricultural sector is undergoing a transformation, driven by smart farming based on precision agriculture (PA) and digital livestock farming (DLF) technologies, but substantial further progress is needed. Smart farming based on digital agriculture principles and innovative governance models requires more precise and real-time platforms for the enhancement of crop production, animal welfare and productivity. For the development of efficient analytics for the dynamic and automated processing of the temporal–spatial distribution of soil, crops, animal sand environments in real time, several challenges need to be overcome. Out of those challenges, scalability for coping with big data and robustness for performance in real-time prediction are the most major. Conventional statistical techniques and approaches, such as random forests or decision trees, may not suffice; hence, there is a need for deep learning and advanced machine learning approaches for tackling the challenges of big data and real-time series prediction for developing novel solutions in the agrifood domain. This Special Issue calls for papers concerning the role of machine vision, sensor-enabled big data applications and implications of neural networks for smart agricultural applications.

Dr. Suresh Neethirajan
Guest Editor

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. Agriculture is an international peer-reviewed open access monthly 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 2600 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

  • digital livestock farming
  • precision agriculture
  • digital agriculture
  • adaptation physiology
  • computer technologies for agriculture
  • agrifood big data analytics
  • precision animal systems
  • farm animal–computer interaction
  • machine learning in animal omics
  • data science in animal omics
  • real-time sensor data for soil, air, crop and livestock

Published Papers (3 papers)

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Research

39 pages, 10321 KiB  
Article
AI in Sustainable Pig Farming: IoT Insights into Stress and Gait
by Suresh Neethirajan
Agriculture 2023, 13(9), 1706; https://doi.org/10.3390/agriculture13091706 - 29 Aug 2023
Cited by 2 | Viewed by 2277
Abstract
This paper pioneers a novel exploration of environmental impacts in livestock farming, focusing on pig farming’s intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, [...] Read more.
This paper pioneers a novel exploration of environmental impacts in livestock farming, focusing on pig farming’s intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs aged 86 to 108 days using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores the potential detection of gait abnormalities, correlations between pigs’ age and their gait or activity patterns, and the evolution of pigs’ walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs’ physiological parameters. Sensor data are further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems. Full article
(This article belongs to the Special Issue Neural Networks and Image Analysis in Intelligent Agricultural Engineering)
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15 pages, 7090 KiB  
Article
Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage
by Xiang Yue, Kai Qi, Xinyi Na, Yang Zhang, Yanhua Liu and Cuihong Liu
Agriculture 2023, 13(8), 1643; https://doi.org/10.3390/agriculture13081643 - 21 Aug 2023
Cited by 7 | Viewed by 4993
Abstract
The spread of infections and rot are crucial factors in the decrease in tomato production. Accurately segmenting the affected tomatoes in real-time can prevent the spread of illnesses. However, environmental factors and surface features can affect tomato segmentation accuracy. This study suggests an [...] Read more.
The spread of infections and rot are crucial factors in the decrease in tomato production. Accurately segmenting the affected tomatoes in real-time can prevent the spread of illnesses. However, environmental factors and surface features can affect tomato segmentation accuracy. This study suggests an improved YOLOv8s-Seg network to perform real-time and effective segmentation of tomato fruit, surface color, and surface features. The feature fusion capability of the algorithm was improved by replacing the C2f module with the RepBlock module (stacked by RepConv), adding SimConv convolution (using the ReLU function instead of the SiLU function as the activation function) before two upsampling in the feature fusion network, and replacing the remaining conventional convolution with SimConv. The F1 score was 88.7%, which was 1.0%, 2.8%, 0.8%, and 1.1% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm, respectively. Meanwhile, the segment mean average precision (segment mAP@0.5) was 92.2%, which was 2.4%, 3.2%, 1.8%, and 0.7% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm. The algorithm can perform real-time instance segmentation of tomatoes with an inference time of 3.5 ms. This approach provides technical support for tomato health monitoring and intelligent harvesting. Full article
(This article belongs to the Special Issue Neural Networks and Image Analysis in Intelligent Agricultural Engineering)
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16 pages, 4242 KiB  
Article
Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses
by Elham Bolandnazar, Hassan Sadrnia, Abbas Rohani, Francesco Marinello and Morteza Taki
Agriculture 2023, 13(8), 1583; https://doi.org/10.3390/agriculture13081583 - 09 Aug 2023
Viewed by 1829
Abstract
Accurate temperature prediction and modeling are critical for effective management of agricultural greenhouses. By optimizing control and minimizing energy waste, farmers can maintain optimal environmental conditions, leading to improved crop yields and reduced financial losses. In this study, multiple models, including Multiple Linear [...] Read more.
Accurate temperature prediction and modeling are critical for effective management of agricultural greenhouses. By optimizing control and minimizing energy waste, farmers can maintain optimal environmental conditions, leading to improved crop yields and reduced financial losses. In this study, multiple models, including Multiple Linear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM), were compared to predict greenhouse air temperature. External parameters, such as air temperature (Tout), relative humidity (Hout), wind speed (W), and solar radiation (S), were used as inputs for these models, and the output was the inside temperature. The results showed that the RBF model with the LM (Levenberg–Marquardt) learning algorithm outperformed the other models, achieving the lowest error and the highest coefficient of determination (R2) value. The RBF model produced RMSE, MAPE, and R2 values of 1.32 °C, 3.23%, and 0.931, respectively. These results demonstrate that the RBF model with the LM learning algorithm can reliably predict greenhouse air temperatures for the next two hours. The ANN model can be applied to optimize time management and reduce energy losses, improving the overall efficiency of greenhouse operations. Full article
(This article belongs to the Special Issue Neural Networks and Image Analysis in Intelligent Agricultural Engineering)
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