Deep Learning Precision Farming: Tomato Leaf Disease Detection by Transfer Learning

5 Pages Posted: 11 Apr 2019

See all articles by Mosin Hasan

Mosin Hasan

BVM Engineering College

Bhavesh Tanawala

BVM Engineering College

Krina J. Patel

BVM Engineering College

Date Written: March 9, 2019

Abstract

Agriculture sector in India is facing few issues like low yield per hectare, traditional techniques of production, fertilizers and pesticides used at great scale. In this research, we implemented drones based precision farming system to effectively identify high disease area in farm using Convolution Neural Network. At later stage, we will spray precise pesticides on affected area based on infection level with the help of drones. The dataset contains 2100 images of tomato leaves from internet and 500 images from local farms. Transfer learning is used to retrain the inception model of Google to train CNN for tomato leaves. Leaves are classified in 3 broad category as good, average and bad for pesticide intensity. 99% accuracy is achieved when training percentage is increased to 85%. Execution speed of system is also fast as we are using transfer learning on inception model.

Keywords: Precision Farming, Tomato Leaf Disease, Deep Learning, Tensor Flow, CNN, Transfer Learning, Drone

Suggested Citation

Hasan, Mosin and Tanawala, Bhavesh and Patel, Krina J., Deep Learning Precision Farming: Tomato Leaf Disease Detection by Transfer Learning (March 9, 2019). Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE) 2019. Available at SSRN: https://ssrn.com/abstract=3349597 or http://dx.doi.org/10.2139/ssrn.3349597

Mosin Hasan (Contact Author)

BVM Engineering College ( email )

VIDYA NAGAR
ANAND
V V Nagar
India

Bhavesh Tanawala

BVM Engineering College ( email )

VIDYA NAGAR
ANAND
V V Nagar
India

Krina J. Patel

BVM Engineering College ( email )

VIDYA NAGAR
ANAND
V V Nagar
India

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