Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

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Forest fire disasters have always been mankind’s constant and inconvenient companion since time immemorial. In the recent past years, managing crisis for example a large scale fire has become a very difficult and challenging task. Things that are common in most of the forest fire that occur at large scale are loss of life (human or animal), loss of vegetation, loss of flora and fauna, and communication failure (if any). Apart from causing a great loss to valuable natural resources of nature forest fire pose a greater risk not only to life of human being but also to the inhabitant’s such as wild life living in the forest. As per National Fire Danger Rating System (NFDRS), if a fire is detected within 6 minutes of its occurrence then it can be easily disposed-off before it turns into a large scale fire. For this a network that can detect fire at a very early stage is required. There are numerous techniques to detect the occurrence of forest fire and this article is dedicated towards reviewing detection techniques present in the literature. This work will give a bird’s eye view of the technologies used in automatic detection of forest fires and reviews almost all the detection techniques available in the literature. To the best of our knowledge this is the first time that almost all the techniques available in the literature are reviewed and considering almost all the parameters.

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