Andere Kunden interessierten sich auch für
![Machine Learning Approaches for Disease State Classification]( "Machine Learning Approaches for Disease State Classification")
Machine Learning Approaches for Disease State Classification26,99 €![Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease]( "Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease")
Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease434,99 €![Impact Analysis of Solutions for Chronic Disease Prevention and Management]( "Impact Analysis of Solutions for Chronic Disease Prevention and Management")
Impact Analysis of Solutions for Chronic Disease Prevention and Management39,99 €![A Supervised Learning Model for Detecting Alzheimer's Disease]( "A Supervised Learning Model for Detecting Alzheimer's Disease")
A Supervised Learning Model for Detecting Alzheimer's Disease32,99 €![Medical Astrology and skin disease: Psoriasis]( "Medical Astrology and skin disease: Psoriasis")
Medical Astrology and skin disease: Psoriasis26,99 €![Analysis of Cardiac MRI to recognize cardiovascular disease by AST]( "Analysis of Cardiac MRI to recognize cardiovascular disease by AST")
Analysis of Cardiac MRI to recognize cardiovascular disease by AST39,99 €![CONCEPT MAP GENERATION FROM DOMAIN TEXT USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES]( "CONCEPT MAP GENERATION FROM DOMAIN TEXT USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES")
CONCEPT MAP GENERATION FROM DOMAIN TEXT USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES24,99 €
The world's largest agricultural need is high production; hence, most countries use modern techniques to boost crop yields. Advanced technology should increase yields. Other factors such as environmental stresses (pests, diseases, drought stress, nutritional deficits, and weeds) and pests affect plants at any stage. Thus, in agriculture, both quantity and quality are reduced. Crop diseases are the most important reason for quality and quantity losses in farming production. Such losses negatively affect the profit and production costs of stakeholders in farming. Conventionally, plant pathologists and farmers utilize their eyes to notice diseases and formulate decisions depending upon their knowledge that are often not precise and at times biased as in the earlier time a lot of types of diseases seems to be similar. This scheme paved the way for the needless usage of pesticides that resulted in high generation costs. Therefore, the requirement for a precise disease detector related to a consistent dataset to assist farmers is essential, particularly for the case of inexperienced and young ones . Advancements in computer vision help with the usage of ML or DL schemes. Moreover, there is a requirement for an earlier disease recognition system for protecting the yield over time. Accordingly, CNN is highly deployed in crop disease detection, and reasonable results are attained. Nevertheless, the crop disease images attained from lands were characteristically uncertain images that have a noteworthy effect on the enhancement of accuracy in crop disease recognition from images. There is a detrimental effect on agricultural output due to the prevalence of crop diseases, and increase food insecurity . The agricultural industry relies heavily on early identification of diseases, that prevention of crop diseases. Spots or scars on the leaves, stems, flowers, or fruits are common symptoms of crop diseases. Most of the time, anomalies can be diagnosed by looking for telltale signs that are specific to a given disease or pest. The leaves of crops are often the first to show signs of disease, making them an excellent starting point for diagnosis