Kullback chi square and Gustafson Kessel probabilistic neural network based soil fertility prediction

Rajamanickam, Jayalakshmi; Mani, S.

doi:10.1002/cpe.6460

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…A supervised ML algorithm is used on training datasets and tested on test datasets, and R Tools is used to execute this algorithm. Rajamanickam & Mani (2021) proposed predicting soil fertility by integrating uncertainty quantification using fisher ratio pre-processing models and Kullback divergent chi-square FS. Then, rather than an individual value, Gustafson-Kessel probabilistic NN classifications use the soil fertility prediction models to generate the likelihood distribution as output and the distinct types of soil fertility levels.…”

Section: Methodsmentioning

confidence: 99%

“…Also, a semi-supervised classification pipeline is introduced that is based on CAE and uses endmember abundance maps as classification features. Real hyperspectral datasets are used for experiments, and supervised and semi-supervised models are used to compare the results ( Rajamanickam & Mani, 2021 ). The layer-wise training is implemented for optimizing parameters of the entire network.…”

Section: Methodsmentioning

confidence: 99%

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Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model

Raman¹,

Chelliah²

2023

PeerJ

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Background Soil nutrients play an important role in soil fertility and other environmental factors. Soil testing is an effective tool for evaluating soil nutrient levels and calculating the appropriate quantitative of soil nutrients based on fertility and crop requirements. Because traditional soil nutrient testing models are impractical for real-time applications, efficient soil nutrient and potential hydrogen (pH) prediction models are required to improve overall crop productivity. Soil testing is an effective method to evaluate the presence of nutrient status of soil and assists in determining appropriate nutrient quantity. Methods Various machine learning (ML) models proposed, predict the soil nutrients, soil type, and soil moisture. To assess the significant soil nutrient content, this study develops an enhanced reptile search optimization with convolutional autoencoder (ERSOCAE-SNC) model for classifying and predicting the fertility indices. The model majorly focuses on the soil test reports. For classification, CAE model is applied which accurately determines the nutrient levels such as phosphorus (P), available potassium (K), organic carbon (OC), boron (B) and soil pH level. Since the trial-and-error method for hyperparameter tuning of CAE model is a tedious and erroneous process, the ERSO algorithm has been utilized which in turn enhances the classification performance. Besides, the ERSO algorithm is derived by incorporating the chaotic concepts into the RSO algorithm. Results Finally, the influence of the ERSOCAE-SNC model is examined using a series of simulations. The ERSOCAE-SNC model reported best results over other approaches and produces an accuracy of 98.99% for soil nutrients and 99.12% for soil pH. The model developed for the ML decision systems will help the Tamil Nadu government to manage the problems in soil nutrient deficiency and improve the soil health and environmental quality. Also reduces the input expenditures of fertilizers and saves time of soil experts.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model

Raman¹,

Chelliah²

2023

PeerJ

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“…Crop damage from climate change and other environmental stresses on farming methods were discussed in [5] by Rajamanickam and Mani. Higher accuracy and lower processing time are provided by the authors' suggested probabilistic neural network for the soil fertility prediction approach.…”

Section: Literature Surveymentioning

confidence: 99%

Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system

Vijender Reddy,

Venkata Krishna Reddy,

Spandana

et al. 2024

E3S Web of Conf.

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Agriculture forms a major occupation in countries like India. More than 75% people rely on farming for their daily wages. Food security on a global scale is mostly dependent on agriculture. Hence, achieving good yield in the crops grown by farmers is the major concern. Various environmental factors have a significant impact on the crop yield. One such component that contributes majorly to the crop yield is soil. Due to urbanization and enhanced industrialization, the agricultural soil is getting contaminated, losing fertility, and hindering the crop yield. One exciting new way to maximise crop yields while decreasing input costs is precision farming, which makes use of machine learning (ML) and the IoT. Machine Learning (ML) is employed for agricultural data analysis. The goal of this research is to optimise agricultural practices by presenting an integrated crop and fertiliser recommendation system. The proposed ML based model “Precision Agriculture” aims at predicting the suitable crops that can be grown based on the class which the soil sample belongs to and suggests the fertilizers that can be used to further enhance the fertility of soil. Using proposed model, farmers can make decisions on which crop to grow based on the soil classification and decide upon the nitrogen–phosphorous– potassium (NPK) fertilizers ratio that can be used. Comparison of the SVM algorithm with Naive Bayes, and LSTM has shown that SVM performed with a higher accuracy. Decision support tools that integrate AI and domain knowledge are provided by the study, which is a substantial contribution to precision agriculture.

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“…In [28], Rajamanickam and Mani addressed the impact of climate anomalies on crops and environmental challenges on agriculture practices. The authors have proposed a probabilistic neural network for the soil fertility prediction approach, providing higher accuracy and reduced processing time.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices

Musanase,

Vodacek,

Hanyurwimfura

et al. 2023

Agriculture

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Agriculture plays a key role in global food security. Agriculture is critical to global food security and economic development. Precision farming using machine learning (ML) and the Internet of Things (IoT) is a promising approach to increasing crop productivity and optimizing resource use. This paper presents an integrated crop and fertilizer recommendation system aimed at optimizing agricultural practices in Rwanda. The system is built on two predictive models: a machine learning model for crop recommendations and a rule-based fertilization recommendation model. The crop recommendation system is based on a neural network model trained on a dataset of major Rwandan crops and their key growth parameters such as nitrogen, phosphorus, potassium levels, and soil pH. The fertilizer recommendation system uses a rule-based approach to provide personalized fertilizer recommendations based on pre-compiled tables. The proposed prediction model achieves 97% accuracy. The study makes a significant contribution to the field of precision agriculture by providing decision support tools that combine artificial intelligence and domain knowledge.

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Kullback chi square and Gustafson Kessel probabilistic neural network based soil fertility prediction

Cited by 10 publications

References 16 publications

Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model

Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model

Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system

Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices

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