Category: Machine learning and deep learning in agriculture

Farm animal production deals with the problem of production system. The foremost scope of machine learning applications in farm animal production is precise judgment of monetary balances with the help of which the producers can get information based on production line monitoring and thus can gain profits. This is because the machine learning algorithms have…

The field of animal welfare takes care of the health and well-​being of animals so as to maintain a balance in the ecosystem. The key application of machine learning is in monitoring animal behaviour during the early exposure of infection.

Irrigation is an important part of agriculture. It plays a significant role in yield productivity. Irrigation should neither be in excess nor less but should be balanced. To maintain these conditions certain factors need to be considered which are soil type, land topography, weather, type of crop, water quality etc.

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