Machine Learning Algorithm based Disease Detection in Tomato with Automated Image Telemetry for Vertical Farming

Anubhove, Md. Sadik Tasrif; Ashrafi, Nawreen; Saleque, Ahmed Mortuza; Akter, Morsheda; Saif, Shadman Uddin

doi:10.1109/compe49325.2020.9200129

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…If a weather monitoring process is present then all necessary environmental parameters should be monitored using a slave controller [11,12] where a Hyper Text Transfer Protocol (HTTP) will be used for sending and receiving the data. Further the usage of natural resources has been extended using a solar panel which is accompanied with water sensors [13][14][15][16][17]. But ancient methods of agriculture, i.e., before the invention of sensors concentration level in crops, are identified using pH controller [18].…”

Section: Fundamental Implementation Of Sensors and Iotmentioning

confidence: 99%

“…is type of integration forms the rudimentary segment for the proposed method for achieving high accurate predicted results. One major drawback in abovementioned method [10][11][12][13][14][15][16][17][18] is that it focuses on implementing a hardware module such as Raspberry pi and different microcontrollers for monitoring different parameters but a real time based solution in vertical farming can be obtained only after monitoring parameters like height, load, and thickness of crops which is discussed in the subsequent sections. Table 1 describes the assessment of existing and proposed techniques.…”

Section: Fundamental Implementation Of Sensors and Iotmentioning

confidence: 99%

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Wireless Sensor Data Acquisition and Control Monitoring Model for Internet of Things Applications

Haleem

Kshirsagar

Manoharan³

et al. 2022

Scientific Programming

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This article focuses on providing solutions for one important application termed as agriculture. In India, one major occupation for people living in urban and rural areas is agriculture where an economic rate depends only on the crops they yield. In such cases, if an intelligent monitoring device is not integrated then it becomes difficult for the farmers to grow their crops and to accomplish marginal income from what they have invested. Also existing methods have been analyzed in the same field where some devices have been installed and checked for increasing the productivity of horticulture crops. But existing methods fail to install an intelligent monitoring device that can provide periodic results within short span of time. Therefore, a sensor based technology with Internet of Things (IoT) has been implemented in the projected work for monitoring major parameters that support the growth and income of farmers. Also, an optimization algorithm for identifying the loss in different crops has been incorporated for maximizing the system boundary and to transmit data to farmers located in different areas. To prove the cogency of proposed method some existing methods have been compared and the results prove the projected technique produces improved results for about 58%.

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Section: Fundamental Implementation Of Sensors and Iotmentioning

confidence: 99%

Section: Fundamental Implementation Of Sensors and Iotmentioning

confidence: 99%

Wireless Sensor Data Acquisition and Control Monitoring Model for Internet of Things Applications

Haleem

Kshirsagar

Manoharan³

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Additionally, cable-driven robot prototypes have been developed to monitor and care for vegetation in hard-to-reach areas [14,34]. Some studies have also demonstrated the application of image processing techniques to identify fruits [34], employ Machine Learning Algorithms for detecting tomato diseases [35], and support decisions on whether the plant can be harvested or not using a simple NDVI threshold [36]. Nonetheless, progress in monitoring outdoor VGS remains limited.…”

Section: Introductionmentioning

confidence: 99%

Toward Precision Agriculture in Outdoor Vertical Greenery Systems (VGS): Monitoring and Early Detection of Stress Events

Zuckerman,

Cohen,

Alchanatis

et al. 2024

Remote Sensing

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Vertical greenery systems (VGS) have been proposed as a nature-based solution to mitigate the adverse effects of urban heat islands and climate change in cities. However, large-scale VGS are costly and require ongoing maintenance, typically carried out manually through trial and error based on professional experience. Advanced management is essential for the sustainability of VGS due to its limited accessibility and associated costs. To address these challenges, we examined the use of remote sensing methods for outdoor VGS monitoring as a basis for a precision agriculture approach for VGS management and maintenance. This study presents the first ongoing monitoring of real-scale VGS using thermal, hyperspectral, and RGB vegetation indices. These indices were employed for the early detection of vegetation stress, focusing on two case studies exhibiting visible yellowing symptoms. Through the application of unsupervised classification techniques, stressed pixels were successfully detected 14–35 days before visual yellowing, achieving an accuracy of 0.85–0.91. Additionally, the thermal index provided valuable information regarding the spatial distribution of watering along the VGS. Stress maps based on noninvasive methods were demonstrated, forming the basis of a spatial decision support system capable of detecting issues related to plant vitality and VGS irrigation management.

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Tomato’s Disease Identification Using Machine Learning Techniques with the Potential of AR and VR Technologies for Inclusiveness

Anubhove

Ahmed

Zeyad

et al. 2022

Studies in Computational Intelligence

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Machine Learning Algorithm based Disease Detection in Tomato with Automated Image Telemetry for Vertical Farming

Cited by 10 publications

References 13 publications

Wireless Sensor Data Acquisition and Control Monitoring Model for Internet of Things Applications

Wireless Sensor Data Acquisition and Control Monitoring Model for Internet of Things Applications

Toward Precision Agriculture in Outdoor Vertical Greenery Systems (VGS): Monitoring and Early Detection of Stress Events

Tomato’s Disease Identification Using Machine Learning Techniques with the Potential of AR and VR Technologies for Inclusiveness

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