Tomato is an important vegetable crop cultivated worldwide coming next only to potato. However, the crop can be damaged due to various diseases. It is important for the farmer to know the type of disease for timely treatment of the crop. It has been observed that leaves are clear indicator of specific diseases. A number of Machine Learning (ML) algorithms and Convolution Neural Network (CNN) models have been proposed in literature for identification of tomato crop diseases. CNN models are based on Deep Learning Neural Networks and differ inherently from traditional Machine Learning algorithms like k-NN, Decision-Trees etc. While pretrained CNN models perform fairly well, they tend to be computationally heavy due to large number of parameters involved. In this paper a simplified CNN model is proposed comprising of 8 hidden layers. Using the publicly available dataset PlantVillage, proposed light weight model performs better than the traditional machine learning approaches as well as pretrained models and achieves an accuracy of 98.4%. PlantVillage dataset comprises of 39 classes of different crops like apple, potato, corn, grapes etc. of which 10 classes are of tomato diseases. While traditional ML methods gives best accuracy of 94.9% with k-NN, best accuracy of 93.5% is obtained with VGG16 in pretrained models. To increase performance of proposed CNN, image pre-processing has been used by changing image brightness by a random value of a random width of image after image augmentation. The proposed model also performs extremely well on dataset other than PlantVillage with accuracy of 98.7%.

番茄是世界上种植的重要蔬菜，仅次于马铃薯。但是，农作物会因各种疾病而受损。对于农民来说，重要的是要知道疾病的类型，以便及时对作物进行治疗。已经观察到，叶子是特定疾病的明确指示。在文献中已经提出了许多用于识别番茄作物病害的机器学习（ML）算法和卷积神经网络（CNN）模型。CNN模型基于深度学习神经网络，与传统的机器学习算法（如k-NN，决策树等）有本质上的区别。虽然预训练的CNN模型表现不错，但由于涉及大量参数，它们的计算量往往很大。本文提出了一个简化的CNN模型，该模型包含8个隐藏层。使用公开可用的数据集PlantVillage，提出的轻量级模型比传统的机器学习方法和预训练的模型具有更好的性能，并且达到98.4％的准确性。PlantVillage数据集包含39种不同的农作物，例如苹果，马铃薯，玉米，葡萄等，其中10种是番茄病。传统的ML方法的最佳准确度为94.9％k -NN，在预训练的模型中使用VGG16可获得93.5％的最佳精度。为了提高所提出的CNN的性能，已经通过在图像增强之后通过将图像亮度改变图像的随机宽度的随机值来使用图像预处理。提出的模型在PlantVillage之外的数据集上的表现也非常好，准确性为98.7％。