Secondary and micronutrients are essential elements that are used by plants in small quantities. Yield and quality of agricultural products increased with secondary and micronutrients application, therefore human and animal health is protected.. Secondary (Ca, Mg, S) and micronutrients especially Zn, B are essential for higher productivity of cotton. Magnesium is essential for the production of the green pigment in chlorophyll, which is the driving force for photosynthesis. It is also essential for the metabolism of carbohydrates (sugars), for cell division and protein formation. Zinc is an element directly affects yield and quality because of functions such as activity in biological membrane stability, enzyme activation ability and auxin synthesis, while, boron plays an essential role in the development and growth of new cells in the growing meristem and also required for protein synthesis where nitrogen and carbohydrates are converted into protein. This article reviews the influence of Mg, Zn and B application on yield and quality aspects of Bt cotton.

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A field experiment was conducted to study the influence of potassium fertilization (K @0, K @60 and K @ 90 kg ha-1) and secondary (Magnesium) and micro nutrients (Zinc and Boron) on growth and yield parameters and yield of cotton hybrid Bt (MRC 7201 BGII) at Hyderabad, India during 2015-16 and 2016-17. The experiment was laid out in a split plot design with three main treatments and eight sub treatments and replicated thrice. Among the potassium levels, K @ 90 kg ha-1 recorded significantly higher plant height (76.86, 106.13 cm), number of seeds per boll (30.3, 30.9), seed cotton yield (1941, 2591 kg ha-1) and stalk yield (1967, 2755 kg ha-1) during 2015 and 2016. Among secondary (Magnesium) and micro nutrients (Zinc and Boron), Mg1%Zn0.5%B0.1% recorded highest plant height (74.65, 102.80 cm), dry weight (221.39, 243.54 g), number of seeds per boll (29.27, 29.87), seed cotton yield (1941, 2591 kg ha-1) and stalk yield (1967, 2755 kg ha-1) during 2015 and 2016.

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In agricultural nations, such as India, where agriculture leads more to India’s Economic growth, it plays a significant part. The prediction of the crop is one of the main tasks in agriculture. Crop prediction methods are employed by detecting different soil parameters and factors connected to the atmosphere for predicting the appropriate crop. The unstable climate exposes farmers to danger in the environment. Therefore the correct history data must be maintained is essential. The data stored may be evaluated to predict agricultural production. In a cloud server, experts analyze sensed data, land type, land, climate, and farmers’ economies with a prediction effect. The method forecasts the use of artificial intelligence algorithms for appropriate crops and fertilizers. A crucial strategy for handling numerous challenges connected to agriculture is the domain of artificial intelligence with its high-quality learning capacity. Technologies to help farmers find better solutions around the world are being created. To benefit from the parallel computational and storage management of huge data sets, the agricultural community must establish an architectural design that would enable the identification of new statistical structures to extract valuable information from data structures. These processes assist to explore the field and different challenges and effectively respond to certain challenges. In the improved integration of diverse data collection types from multiple sources, artificial intelligence offers attractive computing and analytical methods. The main principle of AI and systemic approaches to understanding its use in agriculture are presented in this paper. It also addresses several algorithms for artificial intelligence which may be used to create models to deal with various agricultural problems.