Tomato Leaf Disease Detection Using Deep Convolution Neural Network

Abstract

Plants are a major and important food source for the world's population. Smart and sustainable agriculture should be capable of providing precise and timely diagnosis of plant diseases. This helps in preventing financial and other resource loss to the farmers. Since plant diseases show visible symptoms which a plant pathologist will be able to diagnose through optical observations. But this process is slow and requires continuous monitoring as well as the availability and successful diagnostic capability of the pathologist. To overcome this, in smart agriculture, computer-aided plant disease diagnostic/detection model is used to help increased crop yield production. Common diseases are found in tomatoes, potatoes and pepper plants, some of them are bacterial spot, early blight etc. If a farmer can detect these diseases early, and can apply an appropriate treatment then it will improve crop yield and prevent the economic loss. In this work, we train the dataset on three different deep convolution neural network architecture and found the best suitable model to detect tomato leaf diseases. In order to avoid overfitting of the mode, batch normalization layer and a drop out layer has been included. The proposed Deep CNN is trained with various dropout values and a suitable dropout value is identified to regularize the model. The experimental methodology tested on plant village dataset showed improved accuracy of 96%, even without performing pre-processing steps like noise removal. By introducing batch normalization and dropout layer training accuracy improved to 99% whereas validation and testing accuracy is found to be 98%.

A Review: CNN Based Plant Disease Detection

“…To examine graphical information CNN extremely use. CNN constructed with the capability to automatically and appropriately identify features of the spatial hierarchy from the input data [48]. It consist of numerous layers.…”

Machine and Deep Learning Approaches for Plant Disease Detection: A Comprehensive Review

“…Before identifying plant diseases earlier advancements of computers in terms of Image processing, AI, ML, and DL played a crucial role by enabling to provide of accurate, reliable, and scalable solutions for the identification of diseases, pathogens, viruses, and pests in plants sustainable agriculture sector. The early detection of plant disease helps for better quality and food production to train the machine learning model [83] [20], large and diverse datasets of plant images are collected. These datasets contain images of healthy plants as well as the ones infected with different diseases.…”