Plant Disease Detection Using Convolutional Neural Network

Abstract

When plants and crops are suffering from pests it affects the agricultural production of the country. Usually, farmers or experts observe the plants with eye for detection and identification of disease. But this method is often time processing, expensive and inaccurate. Automatic detection using image processing techniques provide fast and accurate results. This paper cares with a replacement approach to the development of disease recognition model, supported leaf image classification, by the utilization of deep convolutional networks. Advances in computer vision present a chance to expand and enhance the practice of precise plant protection and extend the market of computer vision applications within the field of precision agriculture. a completely unique way of training and therefore the methodology used facilitate a fast and straightforward system implementation in practice. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images to make a database, assessed by agricultural experts, a deep learning framework to perform the deep CNN training. This method paper may be a new approach in detecting plant diseases using the deep convolutional neural network trained and finetuned to suit accurately to the database of a plants leaves that was gathered independently for diverse plant diseases. The advance and novelty of the developed model dwell its simplicity healthy leaves and background images are in line with other classes, enabling the model to distinguish between diseased leaves and healthy ones or from the environment by using CNN. Plants are the source of food on earth. Infections and diseases in plants are therefore a big threat, while the foremost common diagnosis is primarily performed by examining the plant body for the presence of visual symptoms [1]. As an alternative to the traditionally time-consuming process, different research works plan to find feasible approaches towards protecting plants. In recent years, growth in technology has engendered several alternatives to traditional arduous methods [2]. Deep learning techniques are very successful in image classification problems.

An Accurate Plant Disease Detection Technique Using Machine Learning

“…Prakanshu et al [12] (2021) had established an innovative method for disease recognition in plants using deep convolutional networks for leaf image classification. Leveraging advancements in computer vision, their approach provides a fast and accurate means of detecting plant diseases.…”

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

“…Overall, the IoT system provides data flows and is further utilized with AI techniques to integrate the data, interpret the data, perform automatic image analysis and data prediction, etc. Figure 1 illustrates the integration of AI with IoT for better user outcomes [ 30 ].…”

Plantation Disease Detection and Diagnosis

“…Al. [1] authored the importance of early detection of various disease present in plants. Agricultural production is adversely affected by the presence of plant diseases.…”