Author: haroonkhan

To increase the value of crop and reduce the wastage one has to classify quality of crop with minimum error. The penultimate sub-​category for the crop is developed for the identification of characteristics associated with the crop class.

In comparison with the conventional approach for classification of plant using comparison of shape and colour of leaves machine learning can give exact and faster results by analysing the leaf vein morphology which provides additional information about characteristics of leaf. The foremost objective is the automatic recognition and categorization of different plant varieties so as…

The soil management plays a key role in yield efficiency, ecological stability and human health both directly and indirectly. Soil is a diverse natural resource having complex processes and fuzzy mechanism in which the temperature of soil also plays an important role in the precise investigation of climatic variations of an area and its ecological…

For a good yield, prevention of weeds is one of the major tasks. Weed detection and prevention is difficult to discriminate from crops, so machine learning using sensors is used. This technique leads to precise detection and prevention of weeds with less expenditure and also it does not harm the environment.

Pest and disease control is one of the main problems in today’s agriculture. One of the methods to control diseases and pests is to uniformly spray the pesticides over the crops, which requires high efficiency but is not economical, and it also poses the risk of side effects such as ground water contamination and adverse…

There are many factors through which a farmer can get optimum results in agriculture. One of these factors is to predict the yield of crop. This factor includes the fertility of soil, irrigation process, climate conditions and controlling of pests. If the farmer does not follow these four factors correctly during farming, there is a…

Some of the applications used in agriculture sectors are

Principle component analysis converts correlated variables into uncorrelated variables using orthogonal transformation in a statistical procedure. Principle component analysis is used to study the interrelation between a set of variables. This algorithm is used to consider a large dataset of interconnected variables and chooses the set which best suits a model. This type of concentration…

Clustering is dividing data into groups such that every group has similar data. It is basically a collection of data on the basis of similarities and dissimilarities between them. Clustering is important since it regulates the essential grouping among the unlabelled data. Where there is no measure for good clustering, it depends on in what…