Plant Disease Classification using Alex Net

Abstract

—In modern agriculture field, pest and disease identification is a major role of crop cultivation. Image classification by the use of deep convolutional neural networks of training and methodology used the facilitate a quick and easy system implementation. Pests and diseases are a threat to plant production, especially in India, but identification remains to be a challenge in massive scale and automatically. Collecting images from Image Net dataset. The results show that we can effectively detect and recognize the rice diseases and pests including healthy plant class using a deep convolutional neural network, with the best accuracy of 96.50%. The significantly high success rate makes the model a really useful advisory or early warning tool, and an approach that would be further expanded to support an integrated plant disease identification system to work in real cultivation conditions.

Classification of Plant Diseases with Machine Learning

“…This dataset consists of approximately 87,000 RGB images of healthy and diseased plant leaves from 38 different classes. [6] At this point, regardless of the size of the images, we can re-analyze them as we want. The initial size of the images is 256*256.…”

An Overview of Recent Advances in Greenhouse Strawberry Cultivation Using Deep Learning Techniques: A Review for Strawberry Practitioners

“…The field of computer vision has presented agriculturally pertinent solutions and applications, providing autonomous and effective methods for cultivating a variety of plants [13,25,26]. Researchers have extensively researched disease control and several applications utilizing computer vision for pest and disease detection, which can be found in the literature [27][28][29][30][31][32][33]. Several applications for the automatic quality control of harvested fruits have been devised [5,[34][35][36], as fruit quality control systems have gained ground in the field of artificial intelligence.…”