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Bacterial blight, caused by Xanthomonas axonpodis pv malvacearum is one of the most important cotton diseases in south western Punjab which leads to heavy losses especially at early crop phonological stages. An experiment was conducted on a cotton genotype (RS 921) at PAU, Regional Research Station, Faridkot to find out relationship between incidence of bacterial blight with weather parameters during Kharif 2014 and 2015. Disease incidence (DI) was recorded weekly. Correlation results revealed that temperatures (maximum, minimum and mean) were negatively correlated with disease whereas, temperature range (maximum temperature - minimum temperature) was highly favorable (r= 0.86* and 0.78** during 2014 and 2015, respectively). Likewise, increase of DI was positively associated with morning relative humidity. Both years, rainfall did not favoured the disease incidence (r = -0.35 and r = -0.07). Similarly, increase in the duration of bright sun shine hours also adversely affected the disease development. The regression model accurately estimated the disease incidence during different standard weeks of Kharif 2014 and 2015 which was validated in Kharif 2016. Results of present study are supposed to provide basis for forecasting of disease development which will help to develop advance warning system for timely management of bacterial blight. [ABSTRACT FROM AUTHOR]

Indian economy includes the major contribution provided by agriculturally driven activities. There is need to provide the agricultural stakeholders an appropriate, reliable, local, fast, ‘round-the-clock, and precise knowledge, possibly in their native language, in order that they can enhance their decision-making capacity to improve the agricultural productivity. This can be achieved by using mobile phones. Mobile phones are becoming part of the daily routine of farmers and can be used to disseminate this appropriate, multilingual, localized knowledge right at the location of use. In this article, an improved fuzzy rule promotion-based technique to infer advice to farmers has been presented. Using this technique, an Android-based mobile phone application namedmAgIDShas been developed. This application uses a Global Positioning System (GPS) for localized inference and local language selected by the user. The application was tested on the dataset of a disease diagnosis of a paddy crop. The chi-square (χ2) test was used to match the result of the mAgIDS system with the experts’ inferences. It was found that the results of the system and experts match with an accuracy of 95.84% at a 5% level of significance. Twenty-two scientists, working at various locations within Punjab state in India, were contacted for validation of the system, with 15 relevant parameters. It was found that the proposed system is statistically valid. The real-time implementation of the system provides immense and timely help to the farmers in making appropriate decisions for their crop production. [ABSTRACT FROM AUTHOR]