Binary Classification Image Classification Deep Learning Model for PlantaeK Jammu Kashmir Leaf Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The PlantaeK Jammu Kashmir Leaf dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: This dataset contains 2153 healthy and unhealthy plant leaf images for eight different fruits and vegetables. The plants taken for study are the native plants of the Kashmir region of India. Eight different plants, namely Apple, Apricot, Cherry, Cranberry, Grapes, Peach, Pear, and Walnut, are selected for the study based on their commercial and medicinal usage. The leaf is the primary object of reference taken for making the database, as they grow much earlier than fruits and the other plant parts.

In this Take1 iteration, we will construct a CNN model based on the InceptionV3 architecture to predict the leaf’s health state based on the available images.

ANALYSIS: In this Take1 iteration, the InceptionV3 model’s performance achieved an accuracy score of 95.07% after five epochs using the training dataset. The same model processed the validation dataset with an accuracy score of 79.72%.

CONCLUSION: In this iteration, the InceptionV3-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: PlantaeK: A leaf database of native plants of Jammu and Kashmir.

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: KOUR, VIPPON PREET; Arora, Sakshi (2019), “PlantaeK: A leaf database of native plants of Jammu and Kashmir,” Mendeley Data, V2, doi: 10.17632/t6j2h22jpx.2 (

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.