Soil Test Crop Response Based Integrated Plant Nutrition System for  Maize on Vertisol

Suresh, R.; Santhi, R.

doi:10.20546/ijcmas.2018.708.187

Cited by 5 publications

(4 citation statements)

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“…The nitrogen (N) is used in much application for prediction without harming nature just by using the outcome of previous years. Recent papers show DNN algorithms are quite similar to ANN algorithms only varying in number of hidden layers [18]. CNN models showed improved accuracy in classification and LSTM neural network is most widely used model which shows great accuracy for regression analysis [19].…”

Section: Literature Surveymentioning

confidence: 99%

Optimized Farming: Crop Recommendation System Using Predictive Analytics

2023

IJIES

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Agriculture is been majorly contributing to the country's GDP. So, it is foremost important to improve the production quality and quantity to improve the agricultural economy. Using modern technologies like IoT, big data, blockchain, robotics, 5G technologies, etc., farming can be improved by replacing humans in agricultural operations. The proposed work focuses on forecasting of yield and prices of agro-products which will be useful for farmers to increase their productivity and which in-turn increase their economy. Data science techniques can be used to perform this by predicting which crop can be grown for given environmental features and what is the outcome before harvesting period. The proposed model uses computational data-driven approach for crop yield prediction and price forecasting of agro-products. The study uses hybrid approach for crop yield prediction which integrates LSTM and genetic algorithm based evolutionary algorithm known as enhanced long short-term memory (ELSTM). The weight assignment plays a major role in learning the behaviour of the network. The gradient value with respect to weight is calculated for each epoch, and weight is updated to compute the new weight of the network to minimize error. The model accurately predicts the crop yield for the dataset which comprises of features like soil data, rainfall, history of production, fertilizers etc., the accuracy of the model is measured using metric root mean square error (RMSE) and mean absolute error (MAE). In the proposed study, the computation model is used to enhance the knowledge about agricultural yield before the crop sowing period. The proposed model will help farmers and government agencies to improve production. The proposed ELSTM model is compared with other machine learning models such as naïve bayes, decision tree, which shows accuracy between 75% to 80% and the experimental result show the proposed ELSTM model is performing better with 85% accuracy. The price forecasting model is necessary to identify the commodity prices well in advance. The agro-product price forecasting is made using auto-regressive integrated moving average (ARIMA) which uses dataset collected from various agricultural produce market committee (APMC). Auto correlation function (ACF) and partial auto correlation function (PACF) time series plots have been used to assess the proposed model's performance in predicting prices of crops.

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Section: Literature Surveymentioning

confidence: 99%

Optimized Farming: Crop Recommendation System Using Predictive Analytics

2023

IJIES

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“…The nutrient application by STCR-IPNS has been considered as a better approach when compared to the general blanket recommendation and also improves the fertilizer use efficiency and soil health. The fertilizer prescription equations for hybrid pearl millet (Udayakumar and Santhi, 2017) on Inceptisol and maize (Suresh and Santhi, 2018) were Targeted Yield Model-based Balanced Nutrient Recommendation for Barnyard Millet on Inceptisol of Tamil Nadu developed. However, there is a shortfall of information regarding the fertilizer prescription equations for small millets in Tamil Nadu.…”

Section: Introductionmentioning

confidence: 99%

Targeted Yield Model-based Balanced Nutrient Recommendation for Barnyard Millet on Inceptisol of Tamil Nadu

Selvam

Santhi

Maragatham

et al. 2022

IJARe

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Background: Barnyard millet is one of the significant small millets cultivated in India. It has provided a good source of nutrients. Its production is low in low fertile soil. For that study undertaken into supplying balanced nutrition to achieve maximum yield by following soil test crop response-based fertilizer prescription equation. Methods: The Inductive methodology adopted in the field experiment for developing a scientific basis of fertilizer prescription for barnyard millet was performed on Periyanaickenpalayam soil series of Inceptisol soil order of Tamil Nadu during 2019-2020. The initial soil available nutrient status, grain yield, total nutrient uptake by barnyard millet and fertilizer N, P2O5 and K2O and FYM applied were taken into account for calculating four basic parameters for developing fertilizer prescription equation viz., Nutrient Requirement (NR) to produce 100 kg of barnyard millet grains, the contribution of nutrients from soil (Cs), fertilizer (Cf) and FYM (Cfym). Result: The fertilizer prescription equations and ready reckoner were developed to prescribe fertilizer dosage for a range of soil test values for specific yield target of barnyard millet on Inceptisol. It can be adopted on the Periyanaickenpalayam soil series of Tamil Nadu and also in regions with allied soil types.

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“…Among the various methods of fertiliser recommendations, the one based on yield targeting is unique because this method not only considers the soil testbased fertilizer dose but also the level of yield the farmer can achieve if good agronomic practices are followed to raise the crop. This targeted yield approach is also scientifically sound as the balanced fertilisation is ensured not only among the fertiliser nutrients but also the soil available nutrients (Suresh & Santhi 2018). Soil testing and fertilizer recommendation provide a practical tool to synchronize crop nutrient demand and soil nutrient supply (Zhang et al 2013).…”

Section: Introductionmentioning

confidence: 99%