Detection of Potato Disease Using Image Segmentation and Machine Learning

      

ABSTARCT :

Potato is one of the prominent food crops all over the world. In Bangladesh, potato cultivation has been getting remarkable popularity over the last decades. Many diseases affect the proper growth of potato plants. Noticeable diseases are seen in the leaf region of this plant. Two common and popular leaf diseases of the potato plants are Early Blight (EB) and Late Blight (LB). However, if these diseases were identified at an early stage it would be very helpful for better production of this crop. To solve this problem by detecting and analyzing these diseases image processing is the best option. This paper proposes an image processing and machine learning-based automatic system that will identify and classify potato leaf diseases. In this paper, image segmentation is done over 450 images of healthy and diseased potato leaf, which is taken from publicly available plant village database and seven classifier algorithms are used for recognition and classification of diseased and healthy leaves. Among them, The Random Forest classifier gives an accuracy of 97%. In this manner, our proposed approach leads to a path of automatic plant leaf disease detection

EXISTING SYSTEM :

Modern phenotyping and plant disease detection provide promising step towards food security and sustainable agriculture. In particular, imaging and computer vision based phenotyping offers the ability to study quantitative plant physiology. On the contrary, manual interpretation requires tremendous amount of work, expertise in plant diseases, and also requires excessive processing time.

DISADVANTAGE :

Over the last decades, the most practiced approach for detection and identification of plant diseases is naked eye observation by experts. But in many cases, this approach proves unfeasible due to the excessive processing time and unavailability of experts at farms located in the remote areas.

PROPOSED SYSTEM :

In this work, we present an approach that integrates image processing and machine learning to allow diagnosing diseases from leaf images. This automated method classifies diseases (or absence thereof) on potato plants from a publicly available plant image database called ‘Plant Village’. Our segmentation approach and utilization of support vector machine demonstrate disease classification over 300 images with an accuracy of 95%. Thus, the proposed approach presents a path toward automated plant diseases diagnosis on a massive scale.

ADVANTAGE :

? Thus imaging technique combined with machine learning offers a solution to the issue of agricultural productivity and ensures food security. ? So the objective of this work is to develop imaging and machine learning based effective and error-free disease detection system for plant.

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