AI based early identification and severity detection of nutrient deficiencies in coconut trees

Abstract

Coconut trees are vital in various agricultural and economic sectors. Their susceptibility to nutrient deficiencies poses a significant threat to growth and productivity. Traditional nutrient assessment methods are time and labour-intensive, relying on manual inspection and chemical testing. There has been no significant research on detecting nutrient deficiencies in coconut trees. Additionally, assessing deficiency, and severity automatically and recommending suitable fertilizers remain unexplored. This research leverages the YOLOv9 model to identify macro and micronutrient deficiencies in coconut trees and proposes an Image Analysis based Severity Detection (IASD), to determine the severity of these deficiencies. Along with these a Severity Index Calculation Model (SICM) is also introduced that calculates the Severity Index (SI) of these deficiencies. For each identified deficiency, the appropriate fertilizer and its application quantity are suggested. Four deep learning models—RetinaNet, Faster Regional Convolutional Neural Network (Faster R-CNN), You Only Look Once version 5 (YOLOv5), and version 9 (YOLOv9) —were compared for the prediction of nutrient deficiencies in coconut trees using a dataset of 5,720 images of nutrient-deficient coconut tree leaves. YOLOv9 outperformed other models with Accuracy, Precision, and Recall values of 80 %, 98.59 %, and 80.37 %, respectively. Manual verification ensured the correctness of IASD and SICM predictions during model creation, providing farmers and agricultural professionals with a precise, automated tool for managing coconut plantations.