Elaeis Guineensis Image Recognition and Analysis of Characterizing Nutrient Deficiency

Dalila, K A Nur; Asraf, H. Muhammad; Iffah, A. L. Zatul; Kamarudin, Kh; Nooritawati, M.T.; Rizam, M. S. B. Shah

doi:10.1166/asl.2017.7416

Cited by 2 publications

(1 citation statement)

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“…Firstly, feature extraction algorithms are developed to extract features such as the texture and boundary of the leaf are extracted from all the images in the database. Next, several prominent features are extracted namely number of red pixels, entropy and correlations [14]. Further, three different types of classifier are used namely RBF, SVM-RBF and ANN using ten-fold cross-validation method.…”

Section: Methodsmentioning

confidence: 99%

An Agricultural Tele-Monitoring Method in Detecting Nutrient Deficiencies of Oil Palm Leaf

H.¹,

Tahir²,

K.A.³

et al. 2018

IJET

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Nutrient management in oil palm plantation is considered as one of the prominent issues especially for smallholder farmer. The nutrient contained in the tress has always been neglected and untreated and these may cause the trees to suffer from nutrient deficiencies. Therefore, in leveraging the oil yield at the maximum, a telemonitoring system is developed to assess and monitor the lack of nutrients for respective trees. This is done using image processing technique and artificial intelligence in detecting the nutritional deficiencies by analyzing the leaf. The categorization focused by classifying into four major types either as magnesium deficiencies, potassium deficiencies, nitrogen deficiencies or healthy that is based on the oil palm’s leaf surface. This is achieved by extracting the features namely number of red pixels, entropy and correlations. Further, two classifiers specifically support vector machine and artificial neural network is used for classification purpose along with performance measure using accuracy(ACC), Mean Square Error (MSE), Mean Absolute Error (MAE), Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV) based on ten-fold cross-validation. Results attained showed that the best classifier is SVM using RBF kernel (SVM-RBF) that is capable to accurately recognize the nutrient deficiencies with 100% accuracy.

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Section: Methodsmentioning

confidence: 99%

An Agricultural Tele-Monitoring Method in Detecting Nutrient Deficiencies of Oil Palm Leaf

H.¹,

Tahir²,

K.A.³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

Conceptual framework of smart fertilization management for oil palm tree based on IOT and deep learning

Adib

Daliman

2021

IOP Conf. Ser.: Earth Environ. Sci.

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Oil palm trees contribute economic income to the national and community by generating various types of productions. This will cause an expansion of the area for the plantation of oil palm seeds, then contributes to the stability in distributing good quality oil to accommodate the growing population. Furthermore, degradation occurs when the planting of oil palm trees increases rapidly, especially the occurrence of uncontrolled oil palm cultivation. The degradation can cause loss of soil nutrients due to soil erosion. The lack of macronutrients, Nitrogen (N), Phosphorus (P), Potassium (K) and Magnesium (Mg) on oil palm tree may impact on its growth which includes the quality of crops. Traditional approach to detect macronutrients, can also lead to some improper control in turn leads results in reduction in yield. The existing system has given limited information of dataset and slower classification performance due to limited functions. With the adaptability of Internet of Things (IoT) technologies, oil palm tree growth data and fertilization management can be utilizing effortlessly and effectively. The context of conceptual framework comprises the IoT technologies, image processing, machine learning and deep learning which focuses on environmental factors that affecting the young oil palm tree growth that involve temperature, humidity, soil moisture content, light and nutrient. Thus, a study of IoT, machine learning and deep learning for smart fertilization management of oil palm trees is suggested helping and raise the efficiency of oil palm trees management in Malaysia.

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Elaeis Guineensis Image Recognition and Analysis of Characterizing Nutrient Deficiency

Cited by 2 publications

References 0 publications

An Agricultural Tele-Monitoring Method in Detecting Nutrient Deficiencies of Oil Palm Leaf

An Agricultural Tele-Monitoring Method in Detecting Nutrient Deficiencies of Oil Palm Leaf

Conceptual framework of smart fertilization management for oil palm tree based on IOT and deep learning

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