Plant Disease Detection using Region-Based Convolutional Neural Network

TL;DR

This paper presents a lightweight, modified region-based convolutional neural network for automatic detection of tomato plant leaf diseases, enabling efficient deployment in drone-based agricultural monitoring systems.

Contribution

It introduces a novel modification of R-CNN tailored for plant disease detection, achieving satisfactory performance on benchmark datasets.

Findings

Effective disease detection accuracy demonstrated

Model is lightweight and suitable for drone deployment

Potential to reduce crop management costs

Abstract

Agriculture plays an important role in the food and economy of Bangladesh. The rapid growth of population over the years also has increased the demand for food production. One of the major reasons behind low crop production is numerous bacteria, virus and fungal plant diseases. Early detection of plant diseases and proper usage of pesticides and fertilizers are vital for preventing the diseases and boost the yield. Most of the farmers use generalized pesticides and fertilizers in the entire fields without specifically knowing the condition of the plants. Thus the production cost oftentimes increases, and, not only that, sometimes this becomes detrimental to the yield. Deep Learning models are found to be very effective to automatically detect plant diseases from images of plants, thereby reducing the need for human specialists. This paper aims at building a lightweight deep learning model for predicting leaf disease in tomato plants. By modifying the region-based convolutional neural network, we design an efficient and effective model that demonstrates satisfactory empirical performance on a benchmark dataset. Our proposed model can easily be deployed in a larger system where drones take images of leaves and these images will be fed into our model to know the health condition.