AI Crop Predictor and Weed Detector Using Wireless Technologies: A Smart Application for Farmers

Dasgupta, Ishita; Saha, Jayit; Pattabiraman, V.; Parvathi, R.

doi:10.1007/s13369-020-04928-2

Cited by 21 publications

(8 citation statements)

References 12 publications

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“…Influences of field heterogeneity [164] [67,191] Sensor field placement [64,91,167,175,185,192] Effective sensing [91,170,175,183,[193][194][195][196] Security Data security and governance [165,166,168,182,190,192,194,[197][198][199][200][201][202] Hacking attacks [199,200,202,203] Data processing Data analysis [19,166,188,189 [175,185,186] Latency and throughput [19,166,168,184,211] Future research 5G challenges [167,171] Ground robot path planning and execution [212] Over...…”

Section: Miscellaneousmentioning

confidence: 99%

See 1 more Smart Citation

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Alexopoulos,

Koutras,

Ali

et al. 2023

Agronomy

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As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0.

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Section: Miscellaneousmentioning

confidence: 99%

“…The batteries are usually not rechargeable or changeable. Therefore, there is a pressing need for saving energy ( [166,167,169,170]). Most of its energy is spent in transmitting sensed data back to the main station.…”

Section: Iot Challengesmentioning

confidence: 99%

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Alexopoulos,

Koutras,

Ali

et al. 2023

Agronomy

View full text Add to dashboard Cite

show abstract

“…In Ishita Dasgupta, et. al [2], with the help of IoT gadgets, AI techniques are combined for faster and more powerful advice of suitable crops to farmers based on a list of factors considered. So, for detection of undesirable flowers on plants, specifically, weed detection, is applied with the frame-shooting drone and deep getting to know strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

Weed identification using KNN algorithm for paddy field based on machine vision

Chetty¹,

Ramya²,

Mohan³

et al. 2022

Robotics, Automation and Non-Destructive Evaluation

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More than 30 to 40 percent of the population in the world choose agriculture as a primary occupation, due to labor shortages and an increased need to feed the global population. In India, most of the cultivation is rice and wheat. One of the major problems faced by Indian farmers is weed growth. Farmers are not able to remove the weed from the root one by one around the cultivation land. Because of the weed growth on the cultivation land, the soil and growth rate of the crops are damaged and reduced respectively. To overcome this problem, we propose to invert a system that can be used to identify the weed around the cultivation land and remove them from the land. In this paper, three algorithms have been used to train the sample images to get accuracy rates of detection. KNN algorithm was the best among the other algorithms. With the help of a webcam, weed identification was accomplished and identified in the paddy field. The classification and the accuracy are determined and done in Matlab and converted into Python. The geographical location of the weed will be stored in the cloud systems. With proper implementation, the increase in the growth rate of crops with improve profit for farmers.

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“…The recent approaches of combining wireless sensor networks and artificial intelligence models to detect weed could increase agricultural efficiency. As opposed to traditional agricultural methods that require more time, effort and eventually lead to inaccurate outputs and losses, modern agricultural approaches including artificial intelligence and the internet of things will assist farmers in making better decisions and increasing the total of crop yield and efficiency [116]. For example, U-Net, a deep semantic segmentation neural network, was observed to detect weed in crops better.…”

Section: Benefit Of Uav To the Agricultural Industrymentioning

confidence: 99%

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

et al. 2021

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Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

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AI Crop Predictor and Weed Detector Using Wireless Technologies: A Smart Application for Farmers

Cited by 21 publications

References 12 publications

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Weed identification using KNN algorithm for paddy field based on machine vision

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

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