Agricultural Crop Recommendations based on Productivity and Season

Vaishnavi, S.; Shobana, M.; Sabitha, R.; Karthik, S.

doi:10.1109/icaccs51430.2021.9441736

Cited by 34 publications

(8 citation statements)

References 11 publications

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“…However, despite the fact that China is a huge agricultural country, agriculture production remains inefficient on a global scale. Under the condition of rigid food demand growth, China's agricultural production is constrained by arable land and water resources [4,5]. Per capita arable land is 40%, and per capita freshwater resources are less than 28% of the world's average resources [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

Chen

Liu

et al. 2022

Journal of Food Quality

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When analyzing agriculture’s total factor productivity, traditional measurement approaches do not take into account the inefficiency value. The production functions are assumed to be analyzed on basis of the random boundaries, which makes the analysis results inaccurate and unreliable. As a result, this paper proposes an analytical approach for agricultural total factor productivity based on the stochastic block model (SBM), which combines the benefits of statistics and machine learning. It uses the SBM direction distance function and the Luenberger productivity index to measure the agricultural efficiency, total factor productivity, and their components. The research study considers the data from 31 provinces from 2006 to 2018 years. First, one output indicator and six input indicators are established. The time-varying variations of the national agricultural inefficiency value and its source decomposition under variable scale returns are then determined using the SBM-based algorithm of agricultural total factor productivity and the obtained sample data. The changes of the agricultural total factor productivity and its components are comprehensively analyzed. Following an examination of the elements impacting agricultural efficiency and productivity, the socioeconomic development of the agricultural total factor productivity is examined in order to achieve efficient growth.

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Section: Introductionmentioning

confidence: 99%

Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

Chen

Liu

et al. 2022

Journal of Food Quality

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“…The dataset used, contains 163 crops which are produced in 59 African countries. The outcome demonstrated that the J48 algorithm has performed the best [4] . The spatial fluctuation of individual soil nutrients in Kerala's Wayanad district was simulated using geostatistical approaches in this research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A total of 1,20,000 entries from the Tamil Nadu Agriculture Dataset were collected. The user was provided recommendations based on crop productivity and the season in which the crops were grown [6] . The researchers created a fuzzy logic-based software that provides an appropriate quantity of fertilizers to soil depending on nutrient levels.…”

Section: Literature Reviewmentioning

confidence: 99%

Crop, Fertilizer, & Irrigation Recommendation using Machine Learning Techniques

Ajmera¹,

Bhandari²,

Jain³

et al. 2022

IJRASET

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Agriculture is the majority source of income for many people not just in the Indian subcontinent but around the world and hence forms the backbone of the economy. Present-day difficulties like unpredictability in weather conditions, water scarcity, and volatility due to demand- supply fluctuations create the need for the farmer to be equipped with modern day techniques. More specifically, topics like less yield of crops due to unpredictable climate, faulty irrigation resources, and soil fertility level depletions needto be communicated. Hence there is a requirement to modify the abundant agriculture data into modern day technologies and make them conveniently accessible to farmers. A technique that can be implemented in crop yield predictionis Machine learning. Numerous machine learning techniqueslike regression, clustering, classification and prediction can be employed in crop yield forecasting. Algorithms like Naïve Bayes, support vector machines, decision trees, linear and logistic regression, and artificial neural networks can be employed in the prediction. The wide array of available algorithms poses a selection dilemma with reference to the selected crop. The purpose of this study is to investigate how different machine learning algorithms may be used to forecast agricultural production and present an approach in the context of big data computing for crop yield prediction and fertilizer recommendation using machine learning techniques

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“…Agriculture aims to produce food for both human and animal consumption. Various crops like maize, rice, millet, cotton, and paddy are cultivated on the land based on the environmental seasons [1]. In many developing countries, most people depend on agriculture; three-quarters of the population depends on agriculture.…”

Section: Introductionmentioning

confidence: 99%

Cap-DiBiL: an automated model for crop water requirement prediction and suitable crop recommendation in agriculture

Munaganuri,

Rao

2023

Environ. Res. Commun.

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In this technological era, various agriculture techniques are used to predict crop growth and crop recommendations. Some of the approaches depend on the IoT smart agricultural-devices to gather information from surrounding. However, multiple information from the sensor and IoT smart device does not provide a clear vision of data based on the agricultural land. Therefore, the proposed model uses various techniques for crop recommendation and crop water forecast requirement. This proposed model effectively predicts crop water requirements and recommendations for suitable crops. The steps involved in crop water requirement prediction are data pre-processing, feature extraction, feature selection, water requirement prediction and recommendation. Initially, the collected IoT data are pre-processed using data normalization, missing value imputation and one-hot encoding. From the pre-processed data, the features are extracted using the Gated Residual autoencoder (GRA) model, whereas the optimal features are selected using the Chaotic Northern Goshawk Optimization (ChaNgo) algorithm. Based on the farmland details, the crop water requirement prediction and suitable crop recommendation based on the market price are carried out using a novel hybrid deep learning model called Channel capsule-assisted stacked dilated Bi-LSTM (Cap-DiBiL). The channel capsule network predicts the crop water requirement and Stacked dilated Bi-LSTM is used for suitable crop recommendations. Then the proposed model analyses the performance and compares it with several existing techniques to prove the proposed model’s enhancement. The proposed model improves the accuracy of predicting the required water and crop recommendations by 98.18%.

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Agricultural Crop Recommendations based on Productivity and Season

Cited by 34 publications

References 11 publications

Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

Crop, Fertilizer, & Irrigation Recommendation using Machine Learning Techniques

Cap-DiBiL: an automated model for crop water requirement prediction and suitable crop recommendation in agriculture

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