Detection of nutrient deficiency in plants using radial basis function neural network with a modified adaptive Kalman filter

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 pre-processed 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.