  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.
