An experimental investigation to spotting the weeds in rice field using deepnet

Kumar, Jayant; C, Ganesh Babu; Priyadharsini, K.

doi:10.1016/j.matpr.2021.01.086

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…(2021) [67] Lightweight convolutional networks 8 field weeds and seedling maize Self-built CNN Average test recognition accuracy of 98.63% Target Detection Osorio et al…”

Section: Target Detectionmentioning

confidence: 99%

Review of deep learning-based weed identification in crop fields

Hu,

Oliver Wane,

Zhu

et al. 2023

International Journal of Agricultural and Biological Engineering

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Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification.

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“…(2021) [67] Lightweight convolutional networks 8 field weeds and seedling maize Self-built CNN Average test recognition accuracy of 98.63% Target Detection Osorio et al…”

Section: Target Detectionmentioning

confidence: 99%

Review of deep learning-based weed identification in crop fields

Hu,

Oliver Wane,

Zhu

et al. 2023

International Journal of Agricultural and Biological Engineering

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“…Various diseases, such as bacterial blight, blast, sheath blight, and brown spot, can afflict paddy crops [9]. These diseases can lead to considerable yield losses and adversely affect the quality of harvested rice [10]. A significant body of research in the literature is dedicated to understanding the causes, symptoms, and management of these diseases [11] [12].…”

Section: A Paddy Crop Diseases and Their Impactmentioning

confidence: 99%

Impact and Analysis of Disease Spread in Paddy Crops using Environmental Factors with the Support of X-Step Algorithm

Prakash¹,

Srivenkatesh²

2023

IJACSA

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India is an agriculture-based country, with paddy being the main crop cultivated on nearly half of its agricultural lands. Paddy cultivation faces numerous challenges, particularly diseases that affect crop growth and yield. Adult paddy crops are especially vulnerable to diseases caused by various factors, such as the green rice leafhopper, rice leaf folder, and brown plant leafhopper. These insects inflict damage on the paddy crops, restricting their growth and leading to significant losses. This research paper investigates the impact of environmental factors on disease spread in paddy crops, using the X-Step Algorithm for analysis. The study aims to better understand the role of environmental conditions, including air, water, and soil quality, in the development and progression of diseases in rice crops. This knowledge will help to optimize disease prevention and management strategies for improved crop yields and food security. The X-Step Algorithm, a novel machine learning algorithm, was employed to model and predict disease spread, taking into account various environmental factors. The proposed algorithm analyses images of paddy crops either manually captured or taken by sensors to evaluate disease spread and growth in paddy crops. This data-driven approach allows for more accurate and timely predictions, enabling farmers and agricultural experts to implement appropriate interventions.

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