**3.5 Considered machine learning approaches for agriculture**

Several machine learning approaches have become popular for achieving superior and precision agriculture [85, 86]. The following sub-section discusses certain machine learning approaches that have been deployed for achieving enhanced agricultural benefits. In the perspective of machine learning, supervised learning is a phenomenon that encompasses both the input and the sought after target values. Besides, both the input and target data are in labelled form, which offers a learning platform for processing data in the future. Further, when this model is offered a new test dataset (with a similar background) since the model is already trained, it generates the accurate output for the test data. Kaur et al. review the scheme of plant disease diagnosis and taxonomy employing leaf images with the aid of computer vision technologies [87].

## *3.5.1 Belief Networks*

Belief Networks also referred to as Bayesian Networks, are probabilistic graphical models, which are utilised for building models from data or through specialists' outlook. Further, these networks can be a beneficial approach for evaluation and effective decision-making process in the case of agrarian problems. The Belief Networks are built using the Bayes theorem, which in turn supports in computing the input data's posterior likelihood. Belief Networks are more suitable for agrarian applications owing to their capability to reason with inadequate data, and further, they also add new evidence data. Further, Aguilera et al., [88] evaluate the quality of the groundwater by deploying the probabilistic clustering supported by the hybrid Bayesian networks via Mixtures of Truncated Exponentials. Huang et al. [89] established a Bayesian driven averaging technique for offering a trustworthy forecast of maise yields in China. Besides, Cornet et al. [90] established a Bayesian network model for identifying the initial growth and yam yield interactions.

Zhu et al. [91] established the Bayesian networks based model to characterise the connections between the symptoms and harvest maladies. De Rainville et al. [92] devised the naive-Bayesian classifier combined with the Gaussian mixture clustering approach for classifying the weeds from the actual row crops. Stanaway et al. [93] discussed the hierarchical Bayesian framework for the early diagnosis of exotic plant pests attacks and infectious plant diseases. Russo et al. [94] established a Bayesian model for estimating the hydrologic characteristics and irrigation needs in order to devise a sustainable water management scheme for the agrarian lands in Punjab, India.
