Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk

Shaikh, Tawseef Ayoub; Mir, Waseem Ahmad; Rasool, Tabasum; Sofi, Shabir Ahmad

doi:10.1007/s11831-022-09761-4

Cited by 64 publications

(26 citation statements)

References 216 publications

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“…These collect data with a higher resolution even in challenging weather conditions, thus providing accurate fruit health assessment and decision-making. Spraying fungicides using UAVs is a current means of disease control [20]. Researchers are optimizing different parameters such as speed, altitude, duration of the flight, tank capacities, etc., for efficacy.…”

Section: State-of-the-art In the Research Fieldmentioning

confidence: 99%

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A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

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Section: State-of-the-art In the Research Fieldmentioning

confidence: 99%

“…The satellite platform provides free images while covering large areas. These images complement UAV imagery but are restricted to low resolution [20]. Therefore, it is not suitable for Scab detection.…”

Section: Limitations Of Sensors and Factors Causing Visibility Issues...mentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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“…This protocol is lightweight, simple, and can be run on a small bandwidth compared to Hypertext Transfer Protocol (HTTP). According to Nurzaman, A. and his colleagues [27] Wireless sensor network (WSN) is proven to be an economically viable solution for the agriculture domain. Visualization, controlling and commanding of the fusion are performed from node-red web-server interface.…”

Section: Proposed Model Architecturementioning

confidence: 99%

“…Wen-Liang, C. and his colleague [25] proposed RiceTalk AI enabled IOT project to detect non-image rice blast disease. Similarly, a reliable and affordable IoT framework has been proposed by Swagatam, B. and his colleague [26] to impact and digitally revolutionize the farming ecosystem; optimize irrigation, monitoring of sensor signals which is essential for guarantee the control of soil variables [27]. Therefore, the application of IIOT and IoT would help to optimize the utilization of scare resources to enhance production quality and productivity [24].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Multiple Sensors to Implement Precision Agriculture using IoT Infrastructure

Aboneh¹,

Rorissa²

2023

Preprint

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Precision Agriculture is the ability to handle variations in productivity within a field and maximize financial return, optimize resource utilization and minimize impact of the environment. It is also the process of automated data collection, cloud storage and utilization to build robust decision support system. In case of Ethiopia, due to poor communication infrastructure coverage and absence of the state-of-the-art technology in the agriculture sector, implementing precision farming system is a challenging tasks in the domain area. In this work, we proposed a fusion of multiple sensors using IOT and IIOT infrastructure to collect critical data from farming fields to develop precision farming facility for decision makers. The main purpose was to monitor weather variability, automate irrigation process, extract critical soil properties. In addition, we have used time series data collected from sensor devices to build forecasting model. Fusion of multiple IoT device provide a mechanism in the agriculture area to deal with real-time monitoring of crops. It is cost-effective technology and required low-energy with edge computing sensor device. We employed the Message Queuing Telemetry Transport (MQTT) protocol to connect the Industrial/Internet of Things (I/IoT) to the cloud server. The communication between system user and sensor device has been done via cloud using Node-RED platform, web android APIs. The cloud-based Eco-system allows us to aggregate, visualize, and analyze live streams output from each sensor in real-time manner. Finally, we have built time series forecasting model using records collected by each sensor device. Using the multi-variate time series data-set, we have obtained about 99 forecasting accuracy on some important variables. Finally, we have developed mobile and web-based application for the end-user to monitor the proposed system remotely.

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“…Different diseases are encountered during the production of tomatoes. The disease must be identified precisely [21].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Linear Vector Quantization for the Diagnosis of Ground Bud Necrosis Virus in Tomato

Kadam

Dhumale²,

Dhumale

et al. 2022

IJEER

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In this varying environment, a correct and appropriate disease diagnosis including early preclusion has never been more significant. Our study on disease identification of groundnut originated by Groundnut Bud Necrosis Virus will cover the way to the effective use of image processing approach in agriculture. The difficulty of capable plant disease protection is very much linked to the problems of sustainable agriculture and climate change. Due to the fast advancement of Artificial Intelligence, the work in this paper is primarily focused on applying Pattern Recognition based techniques. The purpose is to determine the grade of disease to control by developing a model for the selection of bud blight disease caused by GBNV in tomatoes. The images are classified according to the grade of the disease. Different methods have been applied to make a proper diagnosis by bringing clarity in the diagnostic results. Linear Vector Quantization works well than, Radial Basis Function, Back Propagation Neural Network and Support Vector Machine.

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Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk

Cited by 64 publications

References 216 publications

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Fusion of Multiple Sensors to Implement Precision Agriculture using IoT Infrastructure

Linear Vector Quantization for the Diagnosis of Ground Bud Necrosis Virus in Tomato

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