Browsing by Author "Sathyan, Anish"

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    CNN driven nutrient deficiency detection in plants using real-world leaf images
    (Learning Gate, 2024-09-11) Sathyan, Anish; Palanisamy, P
    In agriculture, our farmers currently lack efficient methods to classify and identify plant nutrient deficiencies or diseases, leading to ineffective remedial measures. However, the potential for early detection of plant nutrient deficiencies using colour gradients or leaf patterns, coupled with the advancements in Image Processing, Deep Learning (DL), and Artificial Intelligence (AI), offers a promising future. This paper presents deep learning-based methods to segregate nutrient deficiencies using leaf images, a potential game-changer for the industry. Our research has the potential to revolutionize plant nutrition management, inspiring a new wave of efficient and effective practices in agriculture. We analyzed the International Plant Nutrition Institute (IPNI) dataset and applied an image augmentation mechanism to enhance the training process with the available dataset. By utilizing Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and learning algorithms, we minimised loss and improved accuracy. The model, incorporating over 1.5 million parameters, has shown promising results in predicting deficiencies accurately. We propose an SGD-based training mechanism to balance localization and classification tasks, a step towards a more efficient and effective approach to plant nutrition management.
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    Detection of nutrient deficiency in plants using radial basis function neural network with a modified adaptive Kalman filter
    (Horizon e-Publishing Group, 2025-10-23) Sathyan, Anish; Palanisamy, P
    The agriculture sector plays a significant role in the national GDP. Global agricultural industries employ advanced techniques for the early detection and characterisation of plant nutrient deficiencies to enhance crop yield. In most agriculture-dependent countries, economic growth is determined by the gross yield of entire plantations. Plants exhibit noticeable symptoms such as changes in leaf colouration, spotting, or altered growth patterns. These symptoms, including yellowing (chlorosis), browning, or stunted growth, indicate potential nutrient deficiencies. Monitoring these variations enables early detection of deficiencies before they significantly affect plant health. Early detection of nutrient deficiencies plays a pivotal role in achieving a speedy recovery from the disease, thereby assuring sufficient improvement in the plant’s yield. This article proposes a method for the early detection of nutrient deficiency in plants based on variations in leaf colour and pattern. It uses 54305 data points with three versions of the same images, differing in colour, greyscale and segmentation. Initially, image preprocessing is carried out using Fuzzy Histogram Equalization (FHE) and a modified adaptive Kalman filter to achieve better results. The preprocessed image datasets are then used to train the proposed Radial Basis Function Neural Network (RBFNN). The proposed RBFNN model demonstrates significant improvements (1.18-16.47 %) in accuracy, precision, specificity, sensitivity and F-score compared to conventional models.