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Special Issue "Neural Networks and Image Analysis in Intelligent Agricultural Engineering"

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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 5007

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Dr. Suresh Neethirajan

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Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
Interests: bio-instrumentation; biosensors; precision livestock farming; wearable sensors; agri-food nanotechnology; animal wearables; food biosensors; bioanalytical devices; microfluidic biosensors; bioelectronics
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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 (5 papers)

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Research

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Open AccessArticle

AI in Sustainable Pig Farming: IoT Insights into Stress and Gait

Suresh Neethirajan

Agriculture 2023, 13(9), 1706; https://doi.org/10.3390/agriculture13091706 - 29 Aug 2023

Viewed by 593

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, 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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Open AccessArticle

Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage

and

Agriculture 2023, 13(8), 1643; https://doi.org/10.3390/agriculture13081643 - 21 Aug 2023

Viewed by 900

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 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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Open AccessArticle

Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses

and

Agriculture 2023, 13(8), 1583; https://doi.org/10.3390/agriculture13081583 - 09 Aug 2023

Viewed by 715

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 Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM), were compared to predict greenhouse air temperature. External parameters, such as air temperature (T_out), relative humidity (H_out), 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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Open AccessArticle

Enhancing Animal Welfare with Interaction Recognition: A Deep Dive into Pig Interaction Using Xception Architecture and SSPD-PIR Method

Jung Hwan Kim

Alwin Poulose

Savina Jassica Colaco

Suresh Neethirajan

and

Dong Seog Han

Agriculture 2023, 13(8), 1522; https://doi.org/10.3390/agriculture13081522 - 31 Jul 2023

Viewed by 714

Abstract

The advent of artificial intelligence (AI) in animal husbandry, particularly in pig interaction recognition (PIR), offers a transformative approach to enhancing animal welfare, promoting sustainability, and bolstering climate resilience. This innovative methodology not only mitigates labor costs but also significantly reduces stress levels among domestic pigs, thereby diminishing the necessity for constant human intervention. However, the raw PIR datasets often encompass irrelevant porcine features, which pose a challenge for the accurate interpretation and application of these datasets in real-world scenarios. The majority of these datasets are derived from sequential pig imagery captured from video recordings, and an unregulated shuffling of data often leads to an overlap of data samples between training and testing groups, resulting in skewed experimental evaluations. To circumvent these obstacles, we introduced a groundbreaking solution—the Semi-Shuffle-Pig Detector (SSPD) for PIR datasets. This novel approach ensures a less biased experimental output by maintaining the distinctiveness of testing data samples from the training datasets and systematically discarding superfluous information from raw images. Our optimized method significantly enhances the true performance of classification, providing unbiased experimental evaluations. Remarkably, our approach has led to a substantial improvement in the isolation after feeding (IAF) metric by 20.2% and achieved higher accuracy in segregating IAF and paired after feeding (PAF) classifications exceeding 92%. This methodology, therefore, ensures the preservation of pertinent data within the PIR system and eliminates potential biases in experimental evaluations. As a result, it enhances the accuracy and reliability of real-world PIR applications, contributing to improved animal welfare management, elevated food safety standards, and a more sustainable and climate-resilient livestock industry. Full article

(This article belongs to the Special Issue Neural Networks and Image Analysis in Intelligent Agricultural Engineering)

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Review

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Open AccessReview

SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals

Suresh Neethirajan

Agriculture 2023, 13(2), 436; https://doi.org/10.3390/agriculture13020436 - 13 Feb 2023

Cited by 2 | Viewed by 1331

Abstract

Sensor-enabled big data and artificial intelligence platforms have the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand, while maximizing the efficiency of production systems on the other. Current digital approaches do not meet these challenges due to a lack of efficient and lack of real-time non-invasive precision measurement technologies that can detect and monitor animal diseases and identify resilience in animals. In this opinion review paper, I offer a critical view of the potential of wearable sensor technologies as a unique and necessary contribution to the global market for farm animal health monitoring. To stimulate the sustainable, digital and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and developing new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI)-based data acquisition and processing platform, the current needs, which have not yet been met, can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to its significant and innovative potential. Full article

(This article belongs to the Special Issue Neural Networks and Image Analysis in Intelligent Agricultural Engineering)

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