Detection and classification of nutrient deficiencies in plants using machine learning

Jose, Anu; Nandagopalan, S.; Ubalanka, Vidya; Viswanath, D. P.

doi:10.1088/1742-6596/1850/1/012050

Cited by 17 publications

(7 citation statements)

References 6 publications

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“…Anu Jose et al [8] delved into nutrient deficiency detection in tomato plants using neural networks. The study employed artificial neural networks to classify nutrient deficiencies in tomato plants, analysing leaf characteristics such as colour and shape.…”

Section: Literature Reviewmentioning

confidence: 99%

Poprawa Zdrowia Upraw Dzięki Cyfrowemu Bliźniakowi Do Monitorowania Chorób I Równowagi Składników Odżywczych

Mummaneni,

Sappa,

Katakam

2024

IAPGOS

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Digital Twins is a digital replica of a physical object to observe its real-time performance, gather data, and recommend corrective actions if required to enhance its performance. This fascinating technological idea is now reaching the agriculture fields to transform farming, by creating digital twins of entire farms. This initiative presents an innovative strategy to enhance crop health and yield by creating a digital twin for paddy fields. The aim is to enable early detection of nutrient deficiencies and leaf blast disease, leading to a transformation in agriculture. Creating virtual replicas of plants and fields, the digital twin harnesses real-time data and advanced analytics to transform the way agricultural systems are managed. By integrating remote sensing, data analytics, and various Internet of Things devices like pH, nitrous, potassium, and phosphorus sensors, coupled with a gateway system, the digital twin provides real-time monitoring and analysis of crop health and nutrient levels. Employing advanced machine learning algorithms, notably Convolutional Neural Networks ensures precise and early detection of nutrient deficiencies and crop diseases. This ground-breaking technology provides timely alerts and actionable insights to farmers, enabling proactive decision-making for optimal crop management. This farmland digital twin represents a transformative approach towards agricultural sustainability and enhancing productivity.

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Section: Literature Reviewmentioning

confidence: 99%

Poprawa Zdrowia Upraw Dzięki Cyfrowemu Bliźniakowi Do Monitorowania Chorób I Równowagi Składników Odżywczych

Mummaneni,

Sappa,

Katakam

2024

IAPGOS

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“…Barbedo et al [26] present a review that explains using proximal images of plants to detect nutrient deficiencies using ML models. Jose et al [27] proposed an ML-based approach in which statistical and gray-level co-occurrence matrix features are obtained, and a neural network is trained on these features to classify nutrient deficiencies. In one of the studies, the authors used color and shape features along with an artificial neural network, k-nearest neighbor (k-NN), and support vector machine (SVM) to classify macro-nutrient deficiency in maize plants [28].…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images

Kolhar,

Jagtap,

Shastri

2024

IJCDS

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Nutrients are vital in ensuring expected crop growth and yield quality. Accurate identification of nutrient deficiencies in plants is essential to provide appropriate supplements of fertilizers. Manual inspection of symptoms and identifying nutrient deficiencies is a tiresome task requiring higher expertise. This paper aims to design and develop a computationally efficient deep-learning model to classify plant nutrient deficiencies accurately. This paper presents an image-based deep-learning framework for nutrient deficiency identification. Three deep learning models, namely the Xception model, vision transformer, and multi-layer perceptron-based (MLP) mixer model, were trained to identify nitrogen (N), phosphorous (P), and potassium (K) deficiencies in rice plants from red-green-blue (RGB) images. The model performance is tested on nutrient deficiency symptoms in rice plants dataset available publicly on Kaggle. All three models achieved nutrient deficiency classification accuracy greater than 92%. The Xception model achieved the highest average accuracy of 95.14% at the cost of approximately 1.2 million total trainable parameters, much less than the vision transformer and MLP mixer model. The Xception model performs better than the other two models in classifying nutrient deficiencies with the least number of total trainable parameters. In the future, these neural networks can be trained and extended to accurately detect and segment nutrient-deficient crop areas in large fields to supply precise fertilizer supplements.

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“…The accuracy of the classification is less than 71%. Another researcher has implemented the ANN classification based on the tomato leaves image using texture extraction, resulting in an accuracy of 88.27% [14]. According to Rahadiyan's research, including the leaf's colour, texture, and shape improves model accuracy.…”

Section: Related Workmentioning

confidence: 99%

Citrus Tree Nutrient Deficiency Classification: A Comparative Study of ANN and SVM Using Colour-Texture Features in Leaf Images

Kamelia,

Khawa,

Nurmalasari

et al. 2024

IJCDS

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Nutrient deficiency in Citrus reticulata (mandarin orange) plants is a critical agricultural issue that has far-reaching implications. Such deficiencies not only compromise the overall health and vigor of the mandarin orange trees but also render them more susceptible to diseases and pest infestations. Addressing this concern is of principal importance to ensure sustainable citrus cultivation and enhance crop yield. In this research, we propose a comprehensive approach to tackle the pervasive problem of nutrient deficiencies in Citrus Reticulata var. Fremont leaves. Our approach leverages cutting-edge techniques in image processing and machine learning to provide accurate and efficient detection of these deficiencies. The image data is divided into four classes: N Deficiency, P Deficiency, K Deficiency and Normal. The file sizes are compressed using a lossless compression method, resulting in an average file size reduction of 96.99%. The second stage involves applying segmentation processes to the images using the Canny and Sauvola methods.Third stage involves extracting colour and texture features from the images. The feature values will be used for the classification process in the next stage. The segmentation process employs two methods, namely Canny and Sauvola, which effectively separate the leaves from the background. The detection process is evident in the feature extraction phase, which utilizes two features: colour and texture.The fourth stage involves the classification process based on the segmentation results methods, performed separately using Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. These process results in four datasets: Canny-ANN, Canny-SVM, Sauvola-ANN, and Sauvola-SVM. The highest accuracy is achieved by the Sauvola-ANN method, with a value of 93.75%.

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Detection and classification of nutrient deficiencies in plants using machine learning

Cited by 17 publications

References 6 publications

Poprawa Zdrowia Upraw Dzięki Cyfrowemu Bliźniakowi Do Monitorowania Chorób I Równowagi Składników Odżywczych

Poprawa Zdrowia Upraw Dzięki Cyfrowemu Bliźniakowi Do Monitorowania Chorób I Równowagi Składników Odżywczych

Deep Neural Networks for Classifying Nutrient Deficiencies in Rice Plants Using Leaf Images

Citrus Tree Nutrient Deficiency Classification: A Comparative Study of ANN and SVM Using Colour-Texture Features in Leaf Images

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