Category: Machine learning and deep learning in agriculture

Companies associated with agriculture sector have great importance in the 21st century because any country depends upon its agriculture sector as it generates big revenue that increases the wealth of the country and hence boosts its economy. New automation techniques are used to boost agriculture sector (see Table 1.1). Company Specialization Location Year Description Blue River…

Generally, in machine learning it is not easy to analyse the unstructured data. For this type of data analysis, applying deep learning methods will be more useful where we can use different types of data formats to make algorithm work. To find the relation between different domains which are interdisciplinary we can use deep learning…

GAN is considered as one of the most useful neural networks in many fields. Mainly GAN is used to find the feature loss in image processing caused by down sampling. When the image is compressed, some of the information may get lost or quality of that image is lost, so we may need to recover…

Land cover classification is the key challenging area in agriculture, it involves recognizing the type and quality of land. In the past, a lot of applications were based on mono-​temporal observation. Mono-​temporal methods are dependent on some factors like weather. To solve the problems related to RNN, a model known as NARX is introduced, where…

CNN is broadly used in agriculture since it has strong capacity for image processing. Major applications of deep learning in agriculture can be classified as plant or crop classification, pest and yield prediction, robot harvesting, monitoring of disaster etc. Mainly disease recognition model for plants can be applied by leaf image and pattern classification. Berkley…

Deep learning has transformed agriculture sector to its new level. Deep learning uses techniques such as conventional neural network, RNN and GAN. This gives better results and encourages agriculture domain. The procedure of deep learning uses processing of images and studying the information with efficient results. The intense growth in deep learning field has shown…

Novelty of generative adversarial networks (GAN) lies in technicality of its design. It is a type of unsupervised machine learning which includes computerized innovation such that to understand the similarities or prototype data in the manner that system produces the result. GANs are smart models to build a productive system by modelling a problem having…

Recurrent neural network (RNN) is a type of neural network where output of the previous loop is considered as input for the current loop. General applications of generative neural network are speech recognition, handwriting recognition, analysis of sequence of data etc. Also, generative neural network automatically generates programming codes that give a predefined objective. Working…

Convolution neural network (CNN) structure is based on feed forward neural network and it is designed on an animal cortex and uses multi-​layered perceptron for this process. In CNN the minimum amount of pre-​processing rectified linear unit activation functions are often used. General applications are image/video recognition, natural language processing, chess etc. Convolution is used…

Deep learning is a modern method that has been successfully applied in various domains. Deep learning has various applications such as image processing and text classification. Since the successful rate of deep learning is very high in other domains, so it is applied to agriculture methods too. Deep learning covers several layers of neural networks…