PLANT NUTRIENT DEFICIENCY DETECTION FROM LEAF IMAGES USING AI/ML - DRIVEN ENHANCED CHANNEL BOOSTED CONVOLUTIONAL NEURAL NETWORK

An early prediction and detection of nutrient deficiency empower farmers to appropriately categorise and apply essential nutrient supplements on time. This work presents a novel methodology built on Transfer Learning (TL) with a Convolutional Neural Network (CNN) to offer enhanced accuracy in the early detection of nutrient deficiency using leaf patterns and colour through an Enhanced Channel Boosted - Convolutional Neural Network (CB-CNN). Leaf features are extracted using Oriented FAST and Rotated BRIEF (ORB) before processing by the proposed CB-CNN. The present work precisely forecasts the type of nutrient deficiency from the leaf images, leaf pattern and leaf shape. It is observed that experimental results show 99.37% prediction accuracy over conventional neural network models. Additionally, there is considerable improvement in other performance metrics, viz., precision, specificity, sensitivity and F-score. The proposed methodology beats its existing counterparts by magnitudes ranging from 1.17% to 10.27%. It is thus clinched that the proposed model outperforms existing neural network models with the highest precision and accuracy.