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The paper presents a universal fault diagnostic expert system frame work. The frame work is characterized by two basic features. The first includes a fault diagnostic strategy which utilizes the fault classification and checks knowledge about unit under test. The degree of accuracy to which faults are located is improved by using fault classification knowledge. The second characteristic is object oriented inference mechanism using message passing. Object orientation in inference mechanism improved inference efficiency. The developed framework demonstrates its effectiveness and superiority compared to earlier approaches using case studies.

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This paper provides a survey on plant leaf disease detection technique by using image processing. Plants play a vital role in a humans life; they fulfil our daily needs from food to breathing, We must take care of plants. India is an agricultural country, and about 70% of people depend on agriculture. Plant disease detection is an emerging field in India as agriculture is an important sector that affects the economy and social life, so leaf disease detection is a significant research topic. Fungi, bacteria, and viruses cause most of the diseases on plants. Due to the diseases on the plant the quality and quantity of agriculture product are reduced. To detect the disease on plants, there is a need of experts, but it is the very costly procedure and time-consuming too. To reduce the cost and for the better results, we are using the automation techniques, which will be very helpful in detecting the disease at early stage. The paper discusses an automatic disease detection technique using soft computing.