Senet Cnn Based Tomato Leaf Disease Detection

Abstract

Plants play important roles in the environment. Various plants are fulfilling many demands and basic requirements of the society. Saving such entities in the society is the uttermost necessity in today’s world to deal with plant degradations. Many diseases in plants are common and thus they face degradation. While mainly dealing with common plant such as tomato and potato plants, it is observed that they very often face bacterial and other diseases. A proper precaution can be made to save plant from such diseases. Thus the early prediction of such different diseases can be made, which can be a massive savings to the farmer as well as for the country economy. This paper has adapted a moderate different approach of convolutional neural network called SENet. In this approach, a hybrid process is discussed which uses the advantage of SENet and CNN layer concept for better classification. CNN is performed by using the number of layer and kernel selections. Classification of data is performed using the traditional CNN approach. In this scenario, the quick process occurrence is performed using suppression of less used information. It tries to add weight to each and every feature map in the layer. This approach is used to check, identify and detect the defects in leaf of tomato. The prime motive of the presented approach is; to obtain simple easiest method for the detection of disease in tomato leaf with use of minimal computing resources. Thus an improved, efficient algorithm can be made use in real time implementation of leaf disease prediction with high accuracy and efficiency parameters.

Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction–Blister Blight for Tea Plant

“…Charles Ferber et al review different spectroscopic techniques like infrared, reflectance, and Ramen for plant disease identifications. Each of these techniques is discussed with its advantages and disadvantages approach is to efficiently utilize the computing resources for the identification of plant diseases [37]. Many solutions were proposed for disease detection using the computer vision approach from the apparent characteristics of the disease symptoms on plants.…”