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：农业在孟加拉国的粮食供给和经济中发挥着重要作用。近年来人口的快速增长也增加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、病毒和真菌等植物病害。早期检测植物病害并合理使用农药和化肥，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，就在整个田地中泛用农药和化肥。这不仅常常增加生产成本，而且有时反而对产量有害。深度学习模型已被证明能够有效从植物图像中自动检测病害，从而减少对人类专家的需求。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。通过修改区域卷积神经网络，我们设计了一个高效且有效的模型，该模型在基准数据集上表现出令人满意的实证性能。我们提出的模型可以轻松部署在更大的系统中，在该系统中，无人机拍摄叶片图像，这些图像将被输入我们的模型以了解植物的健康状况。