Recognition of cotton growth period for precise spraying based on convolution neural network

Wang, Shanping; Li, Yang; Yuan, Jing; Song, Laiqi; Liu, Xinghua; Liu, Xuemei

doi:10.1016/j.inpa.2020.05.001

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…We’re going to process video data more efficiently. At present, computer vision technology has been widely used in agricultural research [ 30 , 31 , 32 , 33 ], such as crop pest detection [ 34 , 35 , 36 ] or pest activity detection [ 37 ], crop disease detection [ 38 ], identification of crop growth [ 39 , 40 ], crop yield prediction [ 41 ], and animal behavior detection [ 26 , 27 , 42 ]. The first four kinds of applications can get good results by processing and analyzing only a few clear images.…”

Section: Discussionmentioning

confidence: 99%

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Zhang

Zhan

et al. 2020

Insects

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Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

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Section: Discussionmentioning

confidence: 99%

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Zhang

Zhan

et al. 2020

Insects

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show abstract

“…In [42], CNN is used to establish a cotton growth recognition algorithm. Confusion matrix and recognition efficiency exploited for the optimization process where a CNN model is established, and its precision was proved by modifying training /test sets based on the concept of the k-fold test.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Review on Classification Methods for Plants Leaves Recognition

Suwais¹,

Alheeti²,

Al_Dosary³

2022

IJACSA

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Plants leaves recognition is an important scientific field that is concerned of recognizing leaves using image processing techniques. Several methods are presented using different algorithms to achieve the highest possible accuracy. This paper provides an analytical survey of various methods used in image processing for the recognition of plants through their leaves. These methods help in extracting useful information for botanists to utilize the medicinal properties of these leaves, or for any other agricultural and environmental purposes. We also provide insights and a complete review of different techniques used by researchers that consider different features and classifiers. These features and classifiers are studied in term of their capabilities in enhancing the accuracy ratios of the classification methods. Our analysis shows that both of the Support Victor Machines (SVM) and the Convolutional Neural Network (CNN) are positively dominant among other methods in term of accuracy.

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“…Cotton is a perennial crop having an indeterminate growth habit. The main developmental stages of the cotton plant are: a) planting, b) plant development, c) flowering, d) boll formation and e) conditions about the end of the season (Wang et al, 2021). Due to climate change, there is a rise in average temperatures, disruptions in precipitation patterns affecting the water cycle, and frequent occurrences of extreme weather events (Ye and Grimm, 2013).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Effects of Climate Change on Cotton Production in Maharashtra State of India Using Statistical Model and GIS Mapping

Sengupta

Thangavel²

2023

Caraka Tani J. Sustain. Agric.

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Cotton is a prominent cash crop cultivated for fiber, edible oil and oil cake. A global environmental issue, like climate change, alters weather parameters necessary for the healthy growth and development of cotton plants, affecting fiber quality and economic yield. The study aims to illustrate the evidence of climate change in Maharashtra and assess its impact on the production of cotton in this region. The study was conducted in the state of Maharashtra, India. Geographic information system (GIS)-based models were created based on the vector data (geopolitical boundaries of the state of Maharashtra and its districts) and the corresponding raster attributes (meteorological data) to examine the changes in the patterns of distribution of temperature, rainfall and severity of drought (Standardized Precipitation Index-SPI) over the study period (1990 to 2015). Further, a statistical multiple linear regression model was developed using district-wise data on yield and climatic parameters obtained from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) to estimate the relationship between the dependent variable (yield of cotton) and the independent variables (annual rainfall and annual mean temperature). GIS modeling and mapping provide evidence of changes in the spatial distribution of rainfall and temperature. Although the regression analysis seems weak, it is acceptable for natural systems because natural systems are complex and often highly variable, making it difficult to create a perfect model. The multiple linear regression model shows that such changes in climatic parameters have a significant negative impact on the economic yield of cotton.

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Recognition of cotton growth period for precise spraying based on convolution neural network

Cited by 14 publications

References 24 publications

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

A Review on Classification Methods for Plants Leaves Recognition

Analysis of the Effects of Climate Change on Cotton Production in Maharashtra State of India Using Statistical Model and GIS Mapping

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