# Assessment of nutrient deficiency of rice plant based on modified ResNet50

## TL;DR

This paper presents a modified ResNet50 model for detecting nutrient deficiencies in rice plants using leaf images, enabling early and accurate diagnosis.

## Contribution

A customized ResNet50 model is proposed for efficient and accurate detection of rice plant nutrient deficiencies from raw leaf images.

## Key findings

- The model achieves 95.52% accuracy and 95% F1 score in diagnosing rice plant nutrient deficiencies.
- It demonstrates high reliability with an MCC of 0.9329 and Kappa of 0.8993.
- The model processes images in 9 seconds with minimal data augmentation.

## Abstract

Rice is the staple food of half of the world’s population. It provides security for food in many developing nations. The rice crop is usually short, and the deficiency in nutrition is a major problem. The deficiency in nutrients in rice plants is due to soil having low fertility, unbalanced pH, or incorrect application of fertilizers. These factors contribute to nutrition deficiency and affect the crop’s growth. The deficiency in nutrients is estimated by observing the crop, leaf’s appearance, and leaf’s growth pattern. In this work, we aim to analyze the crop with image processing tools in computer vision to accurately estimate and solve the problem at an earlier stage. The ResNet50 model is further customized for accurately diagnosing the deficiency from leaf images of rice plants. The customized model gets an accuracy, F1 score and FPR are of 95.52 %, 95 %, and 2.24 % respectively. It also has better reliability with an MCC of 0.9329 and Kappa of 0.8993 with an inference time of 9 s. The model, thus, provides an early-time efficient and accurate solution to the problem, demonstrating the robust feature learning capabilities of the modified architecture on raw, unaugmented image data.