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.

翻译：农业在孟加拉国的食品与经济中扮演着重要角色。近年来人口快速增长，也加剧了对粮食生产的需求。导致农作物产量低下的主要原因之一是细菌、病毒和真菌性植物病害的泛滥。早期检测植物病害并合理使用农药和肥料，对于预防病害和提高产量至关重要。大多数农民在不了解植物具体状况的情况下，就在整片农田中使用通用型农药和肥料，这不仅常常导致生产成本增加，有时甚至对产量造成损害。深度学习模型被证明能够通过植物图像自动检测病害，从而减少对人类专家依赖。本文旨在构建一种轻量级深度学习模型，用于预测番茄植物的叶片病害。通过改进基于区域的卷积神经网络，我们设计了一种高效且有效的模型，该模型在基准数据集上展现出令人满意的实证性能。我们提出的模型可轻松部署于更大的系统中：由无人机拍摄叶片图像，并将这些图像输入模型以获知植物健康状况。