Deep Learning Based Weighted SOM to Forecast Weather and Crop Prediction for Agriculture Application

Mohan, Pushpa; Patil, Kiran Kumari

doi:10.22266/ijies2018.0831.17

Cited by 26 publications

(14 citation statements)

References 12 publications

(17 reference statements)

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“…Though the ML models have been relatively efficient and popular in recent decades, training methods and the amount of feeding data have contributed to their success. More often researchers used 70:30 (training:validation), 80:20, or 90:10 partition to simulate the models [ 11 , 13 , 27 , 42 , 43 ]. The data partition scale for training and testing to be given during the simulation is assumed to be still unexplained and without any principled reason-based calculation.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

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Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes.

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Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

View full text Add to dashboard Cite

show abstract

“…P. Mohan, and K. K. Patil, [27] implemented an efficient dimensionality technique SOM with Latent Dirichlet Allocation (LDA). The climate was accurately predicted by using the dimensionality reduced features, which was given as an input to Deep Neural Network (DNN).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Table 3 shows the performance criteria of different forecasting methods for rainfall forecasting. The proposed DRN is compared with the existing techniques such as hybrid techniques namely BRT, RFR, SVR and GPR [18], DT [21], Deep Learning based Weighted SOM [27] and ESN, SVR, and MGGP [28] and evaluated in the combinations of testing and training percentage like 80% training and 20% testing of collected data. The existing methods mainly focused on predicting rainfall directly without extracting the useful information from unstructured data.…”

Section: Comparative Analysismentioning

confidence: 99%

Analysis and Prediction of Crop Production in Andhra Region using Deep Convolutional Regression Network

Talasila¹,

Prasad²,

Reddy³

et al. 2020

IJIES

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Agriculture planning plays a significant role in economic growth and the food security of agro-based country. Crop yield prediction and selection of crops are the most challenging tasks in agricultural domain and it depends on different parameters such as production rate, market price and government policies. Among the two primary tasks, the crop yield prediction is one of the most demanding tasks for every nation. Due to uncertain climatic changes, farmers are struggling to attain a satisfactory amount of yield from the crops. Many researchers have studied on the prediction of weather, prediction of yield rate of crop, crop classification and soil classification for agriculture planning using statistical methods or machine learning techniques. This study focuses on the prediction of major crops in Andhra Pradesh region and presents an enhanced algorithm known as Deep Convolutional Regression Network (DCRN), which is trained and tested on agricultural data collected from farmers. The experimental results showed that the DCRN method achieved nearly 97% prediction accuracy when compared with existing methods like Decision Tree (DT), Self-Organizing Map (SOM).

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“…Climate variability through ENSO will affect crop yield (Boer and Surmaini, 2020;Ismail and Chan, 2019;Qian et al, 2020), which potentially reduces rice IP. This reveals that the sufficiency of water (through rainwater or irrigation) will determine the choice of cropping pattern (Klemm and McPherson, 2017;Mohan and Patil, 2018;Resiani and Sunanjaya, 2020;Sundaravalli and Geetha, 2016). In Gunungkidul, where water sources are 4).…”

Section: Characteristics Of Climate and Season In Gunungkidulmentioning

confidence: 99%

The Potency of the Rice Crop Index Development through Adjustment of Agroclimate and Water Management Situated in Rainfed Field Gunungkidul

Srihartanto¹,

Widodo²

2020

J.Agromet

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One of the strategies to increase the rice self-sufficiency is by improving the rice crop index (IP) in rainfed field areas. This paper aims to obtain the areas for IP development based on agroclimate information on the rainfed fields in Gunungkidul. The methodology of research was based on descriptive analysis, surveys and interviews involving farmers, researchers, agricultural officers, and village officials. We performed field surveys and interviews in 2016 and 2017. The surveys were carried out by identification and verification of the water sources for agriculture, determination of the appropriate water infrastructure, and determination of the areas affected by rice cultivation development. The results showed that the determination of the beginning of planting season could refer the Modern Integrated Planting Calendar (KATAM) and the estimation of the start of the rainy season by the local meteorological office (BMKG). We estimated that as many of 2,111 ha was suitable for IP 200 development, which may raise the potency of rice production to 10,058 tons (increased by 5.10%). On other hand, the IP 300 only covers the rainfed field areas of 687 ha, which has a potential rice production of 3,294 tons (increased by 1.67%). Further identification and verification are needed regarding the potency of water sources. This will determine which types of proper water infrastructure that must be provided for support the development of IP, hence the national rice self-sufficiency.

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Deep Learning Based Weighted SOM to Forecast Weather and Crop Prediction for Agriculture Application

Cited by 26 publications

References 12 publications

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Analysis and Prediction of Crop Production in Andhra Region using Deep Convolutional Regression Network

The Potency of the Rice Crop Index Development through Adjustment of Agroclimate and Water Management Situated in Rainfed Field Gunungkidul

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