India ranks first in area in onion and second in production next to China, but productivity is low as compared to Netherland, USA, China. Growth rates are widely employed in the field of agriculture as these have important policy implications. To study the trends of growth in area, production and productivity of onion crop compound growth rate were worked out. The study concluded that In India, Even though there was decline in area, increasing trend in production was noticed. It was due to increase in Productivity. In Tamil Nadu, the trend in area, production and productivity of Onion was found to be stable.

"The inclusive book on Agricultural Statistics, Mathematics, and Computer applications explains theoretical aspects and gives in-depth information on various concepts in a sequenced manner. This book consists of Agricultural Statistics, Mathematics and Computer Applications, and agricultural Economics. This book has also brought out multiple Choice Questions in each unit. Unit I – Agricultural Statistics deal with Definition, Frequency distribution Measures of central tendency- Mean, Median, Mode, Geometric mean, Harmonic mean, Measures of dispersion, Range, Quartile deviation, Mean deviation, Standard deviation, Variance, Probability, Conditional probability, Theorem of probability, Bayes theorem, Discrete probability distribution Continuous probability distribution, Distribution- Binomial distribution, Poison distribution, Normal distribution, Sampling methods, Theory of testing hypothesis, Correlation, Regression, Analysis of variance, Design of Experiment Unit II – Mathematics deals with Real and Complex Numbers, Set Theory, Matrices, Eigen values and Eigen vectors, Vectors, Differential Calculus, Integral Calculus, Beta and Gamma Functions, Double Integral, Ordinary Differential Equations, and Interpolation Unit III – Computer Applications deal with Input Devices, Output Devices, Memory, Hardware Software, Classification, Booting, computer Virus, Operating system, DOS commands, and Types of files Windows, MS Word, MS Excel, MS PowerPoint, MS Access, Computer Programming, Algorithm, Programming language, BASIC, FORTRAN, C language, and Bioinformatics. Unit IV – Agricultural Economics consists of principles of economics, Law of demand, factors of production, supply, Factor-Product relationship, Factor-Factor relationship, Product-Product relationship, Law of Equi-Marginal Returns, Cost concepts, Balance sheet, Type and Systems of farming, Farm budgeting, and Depreciation, Agricultural Marketing, market Classification, Marketed surplus, Price spread, market integration, Agricultural Finance includes Classification of credit, Nationalisation of Banks, Regional Rural Banks (RRB)"

No abstract

"The inclusive book on 'Agricultural Economics and Extension' explains theoretical aspects and gives an in depth information of various concepts in a sequenced manner. This book consists of five units, such as Agricultural Economics, Agricultural Extension and Computer applications, respectively. This book has also brought out Multiple Choice Questions in each chapter. Agricultural Economics consists of principles of economics, law of diminishing marginal utility, Law of Equi - marginal utility, Indifference curve analysis, Law of demand, consumer's surplus, production, factors of production, supply, Factor-Product relationship, Factor-Factor relationship, Product-Product relationship, Law of Equi-Marginal Returns, Cost concepts, Balance sheet, Type and Systems of farming, Farm budgeting and Depreciation, Agricultural Marketing, market Classification, Marketed surplus, Price spread, market integration, Agricultural Finance includes Classification of credit, Nationalisation of Banks, Regional Rural Banks(RRB),National Bank for Agriculture and Rural Development (NABARD), Reserve bank of India, 3 R's, 5 C's and 7 Ps of credit, Lead Bank Scheme, Crop Insurance schemes. Agricultural Extension deals with the concepts and importance of Extension Education and Sociology, Rural development programmes and Extension teaching methods and contains Communication principles, Cyber Extension, Adoption and Diffusion of Innovation and Capacity building for Extension personnel, farmers, women and youth. Unit III - Computer applications deals with Input Devices, Output Devices, Memory, Hardware Software, Classification, Booting, computer Virus, Operating system, DOS commands, Types of files Windows, MS Word, MS Excel, MS Power Point, MS Access, Computer Programming, Algorithm, Programming language, BASIC, FOTRAN, C language and Bioinformatics. This book is designed for those students who are appearing for various competitive examinations in agricultural and allied sciences"

Background: Proper diagnosis of a foliar disease is a prerequisite to undertaking any crop protection strategy under field conditions. Poor diagnosis and a delay in confirmation in turn decrease the crop yield and increase the cost of plant protection. In this background, advanced machine learning techniques were used for diagnosis of major foliar diseases in black gram using image detection. Casually, black gram yields are highly reduced due to anthracnose and powdery mildew diseases up to 40-67%. To address the issues, the advanced disease identification method of Convolution Neural Network (CNN) is proposed for automated diagnosis in its early stages to assist farmers. Methods: Disease infected leaf samples and their images were collected from different cultivated areas of Tanjore district, Tamil Nadu, India. The image noises were removed and enhanced to improve the accuracy of the training network. A Convolution Neural Network was built with five layers to work on disease images. The first stage of training is to load the image set for training, establish the learning rate, run the optimizer and compile the training convolution model. The final part is to save the loss and accuracy during the training process and evaluate the accuracy of the model. To improve the training learning rate, the Adam optimizer and RMSprop algorithm are used to dynamically adjust the learning rate. The image dataset holds a total of 2002 images of black gram anthracnose and powdery mildew for evaluation. Result: The experiment result showed that the accuracy of disease detection in black gram is about 92.50 per cent with a Precision: 97.14 per cent, Recall: 87.17 per cent, F1 score: 91.89 per cent which proves that convolutional neural network has a faster training speed and higher accuracy. In addition, the proposed method is less time consuming, an early detection tool for the farmers to identify the anthracnose and powdery mildew in black gram leaf which is essential for the application of proper disease management strategies and reduction of yield loss and aids in promotion of smart agriculture.