Evaluating the Influence of Soil and Environmental Parameters in Terms of Crop Suitability using Machine Learning

Bhimanpallewar, Ratnmala; Narasingarao, M. R.

doi:10.18805/ijare.a-4942

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Because of the extra potassium, plants will show typical Mg and potentially Ca shortage symptoms as a result of the cation imbalance. Similar findings were reported in Bhimanpallewar et al (2022) The formulation of organic fertilisers can result in fertilisers of a standard quality, with relatively balanced levels of NPK elements, increasing the ability of biofresh biological agents to effectively increase the disease suppression index by 30.12 per cent, with an increase in productivity of 14.31 per cent of non-standard organic fertiliser treatments. Additionally, 70.6 per cent of Biofresh biological agents were used effectively the results reported in Wijayanto et al (2022).…”

Section: Particularssupporting

confidence: 83%

Assessment of Macro and Micro Nutrients of Soil in Rice (Oryza sativa L.) Fields in Onattukara Wetlands Regions, Kerala

S.¹,

Sathish²,

S.³

2022

ASD

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Background: The purpose of this study is to determine various topographic features as well as the characteristics of micro and macro nutrients in soil samples in the Onattukara wetland paddy fields’ special agricultural zone. Using the Global Positioning System, the particular locations of several soils sampling areas located in different Onattukara blocks were found (GPS). The macro (N, P, K) and micro (zinc, iron, copper, manganese) nutrients in soil samples taken from various places in the paddy fields region were studied. Methods: The Onattukara special agricultural wetland zone in this field investigation during 2017-2018, GPS technique method was used to locating 16 soil samples were collected. Laboratory methods used for chemical analysis of soil. Result: Nitrogen and phosphorus availability were found to be poor in almost all soil samples, whereas potassium availability was found to be medium. In almost all soil samples, the presence of micronutrients (zinc, copper and iron) was found to be critical. The data shows that soils are lacking in N, P and K, necessitating the addition of additional fertilizer and manures to make them acceptable for cultivation and promote healthy growth.

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Section: Particularssupporting

confidence: 83%

Assessment of Macro and Micro Nutrients of Soil in Rice (Oryza sativa L.) Fields in Onattukara Wetlands Regions, Kerala

S.¹,

Sathish²,

S.³

2022

ASD

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“…They are a crucial step in achieving sustainable intensification (Smith et al, 2022). For many farmers, in particular, soil-crop suitability is a requirement for agricultural improvement (Bhimanpallewar and Narasingarao, 2022). The origin, features, and significance of the soil in relation to crop growth are known through the classification of soil and crop suitability.…”

Section: Introductionmentioning

confidence: 99%

Predictive soil-crop suitability pattern extraction using machine learning algorithms

Soberano,

Pisueña,

Tee

et al. 2023

Int. j. adv. appl. sci.

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Machine learning has experienced notable advancements in recent times. Furthermore, this field facilitates the automation of human evaluation and processing, leading to a reduced demand for manual labor. This research paper employs data mining techniques and Knowledge Discovery in Databases (KDD) to conduct an evaluation and classification of various algorithms for pattern extraction and soil suitability prediction. The study utilizes experimental data, data transformation, and pattern extraction techniques on diverse soil samples obtained from different regions of Negros Occidental, Philippines. Specifically, the Naive Bayes, Deep Learning, Decision Tree, and Random Forest algorithms are selected for the classification and prediction of soil suitability based on the available datasets. The assessment of soil-crop suitability is based on data sourced from the Philippine Rice Research Institute, considering 14 parameters including inherent fertility, soil pH, organic matter, phosphorus, potassium, nutrient retention (CEC), base saturation, salinity hazard, water retention, drainage, permeability, stoniness, root depth, and erosion. The findings indicate that the Random Forest algorithm achieved the highest accuracy rate at 94.6% and the lowest classification error rate at 5.4%, suggesting a high level of confidence in the model's predictions. The model's predictions reveal that most soil samples in the area are only marginally suitable for banana, maize, and papaya crops. Furthermore, the study demonstrates that the majority of soil samples have a low fertility rating, which significantly impacts crop suitability. The information obtained from this study can serve as a basis for local farmers to develop improved soil management programs aimed at ensuring more productive soil. Simultaneously, it can contribute to active soil protection initiatives addressing issues such as acidity and salinity in Negros Occidental, Philippines.

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“…Nonparametric methods such as machine learning (ML) are also used and they have good predictive performance (Chen et al 2017;Olden and Jackson 2002). Therefore, ML methods were widely used in many fields (Bhimanpallewar and Narasingarao 2021;Rana et al, 2021;Zaborski and Grzesiak 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Distribution of Portunus trituberculatus in the Northern East China Sea based on Different Modelling Approaches

Wang

Liu

et al. 2021

IJAR

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Background: Portunus trituberculatus is an important economic crab in the East China Sea. With the increase of tonnage and power of offshore fishing vessels, fishing intensity also increases, which has caused great pressure on P. trituberculatus resources. Protecting P. trituberculatus and achieving sustainable utilisation of resources are urgent problems that need to be solved. Therefore, protection and rational development of P. trituberculatus resources are important to accurately understand its spatial and temporal distribution. Methods: In this study, the temporal and spatial distribution predictive models of P. trituberculatus in the northern East China Sea were built on the basis of three analysis methods (generalised additive model [GAM], random forest [RF] and artificial neural network [ANN]) using bottom trawl survey data and environmental data from 2006 to 2007. The fitting and prediction performances of these three models were compared. Result: Season and sea bottom temperature were the most important factors on the distribution of P. trituberculatus. The fitting performance of ANNs was better than those of GAMs and RFs, but its predictive performance was worse than those of GAMs and RFs. Therefore, RFs was the appropriate model in predicting the distribution of P. trituberculatus in the northern East China Sea. The abundance of P. trituberculatus was significantly higher in summer than in other seasons (P less than 0.01) and generally higher in the northern part of the study area than in the southern part in all seasons.

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Evaluating the Influence of Soil and Environmental Parameters in Terms of Crop Suitability using Machine Learning

Cited by 6 publications

References 6 publications

Assessment of Macro and Micro Nutrients of Soil in Rice (Oryza sativa L.) Fields in Onattukara Wetlands Regions, Kerala

Assessment of Macro and Micro Nutrients of Soil in Rice (Oryza sativa L.) Fields in Onattukara Wetlands Regions, Kerala

Predictive soil-crop suitability pattern extraction using machine learning algorithms

Spatial and Temporal Distribution of Portunus trituberculatus in the Northern East China Sea based on Different Modelling Approaches

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Evaluating the Influence of Soil and Environmental Parameters in Terms of Crop Suitability using Machine Learning

Cited by 6 publications

References 6 publications

Assessment of Macro and Micro Nutrients of Soil in Rice (Oryza sativa L.) Fields in Onattukara Wetlands Regions, Kerala

Assessment of Macro and Micro Nutrients of Soil in Rice (Oryza sativa L.) Fields in Onattukara Wetlands Regions, Kerala

Predictive soil-crop suitability pattern extraction using machine learning algorithms

​Spatial and Temporal Distribution of Portunus trituberculatus in the Northern East China Sea based on Different Modelling Approaches

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Product

Resources

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Spatial and Temporal Distribution of Portunus trituberculatus in the Northern East China Sea based on Different Modelling Approaches