Convolutional neural network as an efficient alternative to supervised learners for modelling plant diseases

Abstract

The high productivity of the agricultural sector is crucial to the Global economy, as well as the national economy. For instance, India increased its nation's GDP by a factor of 17% to 18% in the past few decades. In India, the farming sector is the primary source of income. Numerous insects and illnesses have an impact on plant development, amount, and quality of products. Therefore, it is crucial to find the diseases early in the plant's development. Image processing is used to identify plant diseases and pests. Artificial intelligence, machine learning, and visual analysis have all been used in the past few decades to find and diagnose plant diseases. These computerized techniques are excellent for quickly monitoring vast acreage. In this study, we used the foliage of potato plants to identify diseased leaves affected by early blight, as well as late blight. A Comparison is thus laid between the Convolutional Neural Network (CNN) Model for this purpose, and the standard Supervised Learning Classifiers, like k – Nearest Neighbors, Support Vector Machine, AdaBoost, etc. The comparison is subjected not only towards the traditional Metrics, like Accuracy, Precision, etc. but is expanded to temporal domains as well, that is the time taken to compute the results. Also, the training, and testing split used while incorporating the model is another deciding factor in its performance. Several such training–testing splits are considered for such, and thereby, the convolutional Neural Network Model is found to be better in performance than the Supervised Machine Learners irrespective of the Splitting Ratio of the Training, and the Testing Data.