Applications of Computer Vision and Machine Learning in Agriculture: A State-of-the-Art Glimpse

Habib, Md. Tarek; Raza, Dewan Mamun; Islam, Md. Mohaiminul; Victor, Debasish Bhattacharjee; Arif, Md. Ariful Islam

doi:10.1109/icitiit54346.2022.9744150

Cited by 13 publications

(8 citation statements)

References 10 publications

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“…Machine learning and computer vision technologies have demonstrated immense potential in transforming agricultural practices, especially in plant monitoring [6]. These technologies offer advanced capabilities for early detection and prediction of various plant health conditions, which are crucial for sustainable and efficient agricultural practices.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

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Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important in food production. This paper explores the application of convolutional neural networks (CNNs) to forecast key sensor measurements from thermal images within this context. This research focuses on using thermal images to forecast sensor measurements of relative air humidity, soil moisture, and light intensity, which are integral to plant health and productivity in urban farming environments. The results indicate a higher accuracy in forecasting relative air humidity and soil moisture levels, with Mean Absolute Percentage Errors (MAPEs) within the range of 10–12%. These findings correlate with the strong dependency of these parameters on thermal patterns, which are effectively extracted by the CNNs. In contrast, the forecasting of light intensity proved to be more challenging, yielding lower accuracy. The reduced performance is likely due to the more complex and variable factors that affect light in urban environments. The insights gained from the higher predictive accuracy for relative air humidity and soil moisture may inform targeted interventions for urban farming practices, while the lower accuracy in light intensity forecasting highlights the need for further research into the integration of additional data sources or hybrid modeling approaches. The conclusion suggests that the integration of these technologies can significantly enhance the predictive maintenance of plant health, leading to more sustainable and efficient urban farming practices. However, the study also acknowledges the challenges in implementing these technologies in urban agricultural models.

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Section: Related Work and Research Gapsmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

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“…The random forest algorithm, a preeminent decision tree model, has been harnessed extensively within the agricultural sector, particularly in the areas of crop yield prediction and plant disease detection. This ensemble learning methodology constructs a multitude of decision trees during the training phase, culminating in a prediction that reflects either the mode of the classes (classification) or the mean prediction (regression) of the individual trees [46]. A distinct asset of the random forest algorithm is its proficiency in decoding intricate patterns hidden within complex, multivariate agricultural datasets, thereby enabling accurate predictions of pivotal outcomes such as crop yield or disease manifestation [46].…”

Section: Applications Of Machine Learning Algorithms In Agriculturementioning

confidence: 99%

“…This ensemble learning methodology constructs a multitude of decision trees during the training phase, culminating in a prediction that reflects either the mode of the classes (classification) or the mean prediction (regression) of the individual trees [46]. A distinct asset of the random forest algorithm is its proficiency in decoding intricate patterns hidden within complex, multivariate agricultural datasets, thereby enabling accurate predictions of pivotal outcomes such as crop yield or disease manifestation [46]. This aptitude equips random forest with the capacity to provide an unparalleled degree of precision and predictive power in the domains of agricultural planning and disease control.…”

Section: Applications Of Machine Learning Algorithms In Agriculturementioning

confidence: 99%

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

Balatsouras,

Karras,

Karras

et al. 2023

Sensors

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In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications.

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“…The field of computer vision has presented solutions and applications relevant to the agricultural area, offering autonomous and effective methods of cultivating various different plants [ 7 ]. Researchers have widely studied disease control, and it is possible to find in the literature several applications that use computer vision for pest and disease detection [ 8 ]. Fruit quality control systems have also been gaining ground in the AI field, causing several applications to be developed for the automatic quality control of harvested fruits [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Strawberry Farming Using Edge Computing and IoT

Cruz

Mafra

Teixeira

et al. 2022

Sensors

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Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed.

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Applications of Computer Vision and Machine Learning in Agriculture: A State-of-the-Art Glimpse

Cited by 13 publications

References 10 publications

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

Smart Strawberry Farming Using Edge Computing and IoT

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