Advanced Detection: Fungi-Bacterial Diseases in Plants Using Modified Deep Neural Network and DSURF

Introduction

In this world, plants are the core part of the survival of living creatures like human beings and animals because they provide us food. In short, food is a necessity and basic need of each and every human being so that they can get good nutrition’s to keep them healthy and energetic. There are several categories and qualities of foods that can get from plants that can be utilized by living creatures like fruits, vegetables, meat, pulses, dairy products and so on. Quality of food is the foremost important factor in today’s competitive food industry. The demand of food is directly proportional to the quality of food. As excessive the food’s high-quality is, the higher probabilities of demand for that particular food in marketplace nowadays (Choudhary et al., 2022). So, as soon as the people focus on quality of food, the greater the monitoring of food quality was enhanced also. FAO (Food and agriculture report of the United Nations) evaluations shows that every year around 20 to 40 percent of crop productions are damaged globally due to fungi and other diseases found in plants (Brooks et al., 2022). Annually, illness and other diseases of plants cost’s the world’s economy around $220 billion (Rs 22 crore approximately) and fungi-bacterial insects around US$70 billion (Rs 7 crore approximately). It was found that annually around such crores rupees spends into the illness treatment of plants globally. It was shown that 10 out of 100 human beings sick due to food they eat which is getting from plant suffered from fungi and bacterial diseases results death of person also. According to the WHO, around 4,20,000 people die every year due to the consumption of bacterial fungus foods getting from diseased plants. This study gives us the reason to take necessary steps on the improvement of curing towards global spread of plants bacteria and diseases in order to decrease mortality rate in the world (Yan et al., 2022). So, its obligatory to cognizance on the appropriate standard of edibles which begins from the very simple and primary step, wherein the food is kept and produced (food warehouses). Food quality can be assessed on several parameters like visual appearance, size, color, shape and texture of that food (Chakrabarti et al., 2022). There are some specialized food inspectors also in food industries who are taking care of the quality of food manually, but this process is very time consuming and costly. Many times, food inspector found the diseased food (foul smell and bad taste) due to fungi bacterial diseases of plants. So, it’s our responsibility to find out some new and advanced methods that decline the mortality rate and health issues also which ensures the safety of food comes from the fungi diseased plants. Artificial intelligence is a technique which imitates the functioning of as same as human brain. We can use the techniques of man-made intelligence like computer learning and (DL) deep learning in the market of food industry for numerous purposes like sorting and preparation of food, service delivery operation of edibles, improvement in the safecty and the quality of food because all ill food comes from fungi and diseased plants (Xing et al., 2022). Machine learning and deep learning models are using nowadays by several government agencies in order to make predictions to get high efficiency in complex processes like safety of food and quality of food. Many derived conclusions such as convolution networks neural(CNN), also artificial networks neural (ANN) are frequently applined in industry of edibles to solve several problems arises by fungi and diseased plants. ANN is found to be an excellent tool for assessing the food which is produced from diseased plants. We can use advanced detection techniques like deterministic probability-based data fusion in finding fungi and bacterial diseases in plants (Ali & Abdin, 2021). The aim of this paper is to find classified and optimistic solutions using CNN, deep learning, ANN, ML, clustering and deterministic probability-based data fusion in order to find fungus and bacteria in plants so that humans can get fresh food not diseased food. Figure 1 shows the basic steps required to classify fungi-bacterial diseases in plants.

Figure 1.

Basic steps for plant disease detection and classification