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.
农业在孟加拉国的食品和经济中扮演着重要的角色。多年来,人口的快速增长也增加了对粮食生产的需求。导致粮食产量低的主要原因之一是许多细菌、病毒和真菌的植物疾病。及早检测植物疾病和适当使用农药和肥料是至关重要的,以预防疾病并提高产量。大多数农民在整个农场上都使用通用的农药和肥料,而并不具体了解植物的健康状况。因此,生产成本往往会增加,而且有时这会对产量产生不利的影响。深度学习模型被发现非常有效地从植物图像中自动检测植物疾病,从而减少了人类专家的需求。本文的目标是构建一个轻量级的深度学习模型,用于预测西红柿植物的叶病。通过修改基于区域的卷积神经网络,我们设计了一个高效且有效的模型,在一个基准数据集上表现出令人满意的 empirical 性能。我们提出的模型可以轻松地部署在一个更大的系统中,该系统中无人机会拍摄叶图像,这些图像将输入我们的模型以了解健康状况。