Development of fusarium wilt resistant mutants of Musa spp. cv.Rasthali (AAB, Silk subgroup) and comparative proteomic analysis along with its wild type.

MAIN CONCLUSION: Induced mutagenesis using embryogenic cell suspension (ECS) explants with toxin based screening is an effective tool to create non-chimeral Fusarium wilt resistant mutants in banana. Global proteomics unravel the molecular mechanism behind resistance. Race 1 of Fusarium wilt is a serious threat to Musa spp. cv.Rasthali (AAB, Silk subgroup) which is a choice variety traditionally grown in most of the south East Asian countries. Resistant gene introgression into susceptible varieties through conventional breeding has several limitations and the predominant ones being sterility and long generation time. Under such circumstances, induced mutagenesis combined with toxin based in vitro screening remains as the viable alternative for the development of fusarium wilt resistant Rasthali. Therefore, induced mutagenesis was attempted by using ethylmethane sulfonate (EMS) in embryogenic cell suspension (ECS) of Rasthali followed by in vitro screening for fusarium wilt resistance using new generation toxins and pot screening through challenge inoculation with Foc race 1. This ultimately resulted in the identification of 15 resistant lines. Global proteomic analysis in one of the resistant mutant lines namely NRCBRM15 and its wild type revealed 37 proteins, of which 20 showed differential expression. Out of 20 proteins, nineteen were significantly abundant in NRCBRM15 and only one was abundant in wild Rasthali. A total of nine genes based on protein expression were further validated using quantitative real time polymerase chain reaction (qRT-PCR). Annotation results revealed that some of the genes namely Enolase, ATP synthase-alpha subunit, Actin 2, Actin 3,-glucanase, UTP-glucose-1-phosphate uridylyltransferase, Respiratory burst oxidase homolog, V type proton ATPase catalytic subunit A and DUF292 domain containing protein are involved in diverse functions such as carbohydrate metabolism, energy production, electron carrier, response to wounding, binding proteins, cytoskeleton organization, extracellular region, structural molecule and defense.

Mobile application using DCDM and cloud-based automatic plant disease detection.

Farming has a plethora of difficult responsibilities, and plant monitoring is one of them. There is also an urgent need to increase the number of alternative techniques for detecting plant diseases, which is now lacking. The agriculture and agricultural support sectors in India provide employment for the great majority of the country's people. In India, the agricultural production of the country is directly connected to the country's economic growth rate. In order to sustain healthy plant development, a variety of processes must be followed, including consideration of environmental factors and water supply management for the optimal production of crops. It is inefficient and uncertain in its outcomes to use the traditional method of watering a lawn. The devastation of more than 18% of the world's agricultural produce is caused by disease attacks on an annual basis. Because it is difficult to execute these activities manually, identifying plant diseases is essential to decreasing losses in the agricultural product business. In addition to diagnosing a wide range of plant ailments, our method also includes the identification of infections as a prophylactic step. Below is a detailed description of a farm-based module that includes numerous cloud data centers and data conversion devices for accurately monitoring and managing farm information and environmental elements. This procedure involves imaging the plant's visually obvious signs in order to identify disease. It is recommended that the therapy be used in conjunction with an application to minimize any harm. Increased productivity as a result of the suggested approach would help both the agricultural and irrigation sectors. The plant area module is fitted with a mobile camera that captures images of all of the plants in the area, and all of the plants' information is saved in a database, which is accessible from any computer with Internet access. It is planned to record information on the plant's name, the type of illness that has been afflicted, and an image of the plant. In a wide range of applications, bots are used to collect images of various plants as well as to prevent disease transmission. To ensure that all information given is retained on the Internet, data is collected and stored in cloud storage as it becomes essential to regulate the condition. According to our findings from our research on wide images of healthy and ill fruit and plant leaves, real-time diagnosis of plant leaf diseases may be done with 98.78% accuracy in a laboratory environment. We utilized 40,000 photographs and then analyzed 10,000 photos to construct a DCDM deep learning model, which was then used to train additional models on the data set. Using a cloud-based image diagnostic and classification service, consumers may receive information about their condition in less than a second on average, with the process requiring only 0.349 s on average.

Multiparameter optimization system with DCNN in precision agriculture for advanced irrigation planning and scheduling based on soil moisture estimation.

Agriculture is a distinct sector of a country's economy. In recent years, new patterns have evolved in the agricultural industry. In conjunction with sensor scaling down and precision agriculture, the field of remote sensor networks, such as the wireless sensor network (WSN), was developed. Its major purpose is to make horticultural operations simpler to identify, assess, and manage. This paper uses the proposed DCNN to predict soil moisture and plan irrigation for precision agriculture farmers to reduce water consumption used for cultivation and increase production yield by comparing water content during various stages of plant growth and integrating IoT applications into agriculture. It also optimizes the water level for future irrigation decisions to maintain crop growth and water stability. The data must be served and stored in the form of a grid view, according to Apriori and GRU (gated recurrent unit). Using numerous sensor and parameter modelling methodologies, this system assists in the prediction of irrigation planning based on irrigation needs. The predicted parameters include soil moisture, temperature, and humidity. This observed experimental data supports smart irrigation in crop production with a high yield and little water use. DCNN has a 98.5% experimental result accuracy rate and the MSE value is predicted in DCNN 99.25% of the time.

Performance evaluation of deep learning techniques for lung cancer prediction.

Due to the increase in pollution, the number of deaths caused by lung disease is rising rapidly. It is essential to predict the disease in earlier stages by means of high-level knowledge and acquaintance. Deep learning-based lung cancer prediction plays a vital role in assisting the medical practioners for diagnosing lung cancer in earlier stage. Computer-Aided diagnosis is considered to bring a boost to the field of medicine by tying it to automated systems. In this research paper, several models are experimented by using chest X-ray image or CT scan as an input to detect a particular disease. This research work is carried out to identify the best performing deep learning techniques for lung disease prediction. The performance of the method is evaluated using various performance metrics, such as precision, recall, accuracy and Jaccard index.