Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

Tagel-Din, A. E.; Adly, D.; Mostafa, Hassan; Mohammed, Haitham S.

doi:10.1101/2020.09.17.301168

Cited by 12 publications

(10 citation statements)

References 7 publications

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“…They before shipped the dossier to a machine-education invention to produce a warning meaning before effecting both the dossier and the warning communication by way of a graphical user interface (GUI). Several machine learning techniques have been built by Tageldin et al [ 8 ] to predict plant infestation with CLW. The current research established a framework for machine learning to forecast CLW infestation in greenhouse plants.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, environmental sensing parameters such as greenhouse gases, temperature, soil moisture, and light must all be maintained and monitored. This issue could be solved by implementing an Internet of Things (IoT) innovation in intelligent agriculture, which entails the precise application of certain greenhouse factors for optimal plant development, such as temperature management, water flow control, and light radiation, among other things [ 8 , 9 ]. The main contributions of the paper are as follows.…”

Section: Introductionmentioning

confidence: 99%

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IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…While rainfall had a significant and opposite impact, temperature strongly and positively correlated with the frequency of leafhoppers and whiteflies. The genotypes were utterly identical to one another [29]. The evolutionary history and ecological ramifications of the Bemisia tabaci complex have been studied in Ecuador.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Cotton Whitefly Population Using Deep Learning

Soomro

Naeem

Shahzad

et al. 2022

JCBI

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Agricultural is the primary source on which a country's economy depends. Pakistan is the world's fourth-largest cotton producer, making it one of its top cash crops. They can't figure out what sort of cotton is best for their climate because of government regulations, crop risks, and a low literacy rate among the general public and farmers. Using a model that considers temperature, this system attempts to describe the different types of plants. More than anything else, the pest's population is concerned with its diversity, how the weather affects its population, and when it is high or low. The primary purpose of this study was to develop a framework that can handle the complex process of the population of cotton white fly. Our main aim was to know the population of insects with their eggs and with their parents. And another purpose is to find out information about the variety with a low population of insects. To get early knowledge about the people against the temperature. So, cotton yield could be increased, and fewer chemicals should be used. Therefore, farmers' income could be improved. So, we will make the best model for predicting whiteflies on cotton ARIMAX. This model's accuracy was nearly on par with the statistical forecasting models. Because ARIMAX is a statistical model, it may also be used for forecasting.

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“…In addition, this algorithm can improve the generalization ability of the model, thereby providing new ideas and methods for the detection and prediction of crop diseases [22]. Ahmed et al [23] used the XGBoost algorithm to predict the occurrence of vegetation infestation by cotton leaf miner with a prediction accuracy of 84%, which was higher than the prediction results obtained by algorithms such as RF and logistic regression. Sandino et al [24] identified healthy and infested trees in forests by exotic pathogens through the XGBoost algorithm with a classification accuracy of 97%.…”

Section: Introductionmentioning

confidence: 99%

Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

et al. 2022

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Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme gradient boosting (XGboost) method for early and mid-term wheat stripe rust detection based on the vegetation indices extracted from canopy level hyperspectral measurements. Initially, 21 vegetation indices that were related to the early and mid-term winter wheat stripe rust were calculated on the basis of canopy level hyperspectral reflectance. Subsequently, the optimal vegetation index combination for disease detection was determined using correlation analysis (CA) combined with RF algorithms. Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. The vegetation index combinations determined by the single CA algorithm were also used to construct detection models. Compared with the detection models based on the vegetation index combination obtained using the single CA algorithm, the overall accuracy of the four detection models based on the optimal vegetation index combination based on CA combined with RF algorithms increased by 16.1% (XGBoost), 9.7% (RF), 8.1% (SVM), and 8.1% (BPNN). Among the eight models, the XGBoost detection model based on the optimal vegetation index combination using CA combined with RF algorithms, CA-RF-XGBoost, achieved the highest overall accuracy of 87.1% and the highest kappa coefficient of 0.798. Our results indicate that the RF combined with XGBoost can improve the detection accuracy of early and mid-term winter wheat stripe rust effectively at canopy scale.

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Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

Cited by 12 publications

References 7 publications

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Forecasting Cotton Whitefly Population Using Deep Learning

Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

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