Prediction of Crop, Fertilizer and Disease Detection for Precision Agriculture

The main walk of life of our Country is Agriculture. More than 70 % of the population’s lives depend upon agriculture. It is also a great source of country’s economy. In order to make this filed more profitable for farmers proper crops have to be grown in their fields. The prevalent problem among the farmers is Crop choice depending upon the soil in their farmlands. Another challenge faced by farmers is choosing the right fertilisers for their crops, which plays a very important role in getting a good and profitable yield. There is another major problem which they have to give more attention is the pest control or the diseases to which the plants may limit their growth. The above listed problems may solved using the advanced techniques of Precision Agriculture and data mining. Precision Agriculture is modern technique which can be used for farming. The main objective of is to solve above problems using data mining techniques and build a decision system which would help farmers to choose right crops for their farm , fertiliser recommendation for the crops grown and also to help the farmers in detecting the diseases by using the infected leaf images.. Keywords: Precision Agriculture, Data Mining, Crop, fertilizer recommendation system, ML Algorithms

Climate, Cultivation, and Household Income

Maps, figures, and tables present data on rainfall trends and distribution in this Sahelian setting, demonstrating the high level of variability within and between years. The pattern of soils, vegetation and water are described and how these provide the basis for the economy of village and household. Land use, crop choice, and livestock ownership are discussed alongside ownership of other productive assets, such as wells, plough-teams, and donkey-carts. Overall production of the main crop, millet, is described and its allocation to a range of purposes, with a picture of how many days of grain are available from the harvest for different households. Household income and expenditure are shown, the principal sources of income, and cash expenditure by category of purpose, such as purchase of livestock, marriage costs and payment of taxes. It is shown that size of income is partly a function of household size, but also the result of special circumstances, such as whether a marriage is in preparation, or a new ox needs to be bought.

Measuring On-Farm Phosphorus Fertiliser Use—Lessons Learned from Surveying Data of Five Regions in Northern Germany

Phosphorus (P) is essential for agriculture; however, local P surpluses can have adverse environmental effects, such as eutrophication. Optimal P fertiliser use, therefore, means balancing these effects. Although P fluxes from soil to plants are key research areas, little is known about on-farm use of P fertilisers. As, typically, not all fields or crops are treated with P annually, the transferability of measurements for other nutrients, such as nitrogen, appears limited. This study aims to close this knowledge gap. On-farm P use was described using the farm records of 50 farms in five regions of northern Germany for the period of 2010 to 2018. All sources for P fertilisation were taken into account as either P mineral, P organic or P total. Two indicators for on-farm P use were suggested: frequency, which gives the percentage of the treated area, and amount, which gives the quantity used per ha of the treated area. The frequency for P total ranged from 55.9% to 93.1% of the total farm area being fertilised. Amounts between 24.8 and 41.6 kg ha−1 P total were applied on the treated area of the farm. The results supported the separation of the quantity and frequency in on-farm P use. No decrease in P use was found during the period investigated. Using mixed models, the results further show that explanatory variables, including the farm characteristics and crop choice, explain the substantial variations in P use. It is recommended for the example of Germany to establish an official digital database for P fertilisers that can be updated professionally and is mandatory for all documentation on P use.

Revisiting the link between cereal diversity and production in Ethiopia

Studies show that cereal diversity positively affects mean yields, suggesting increased crop diversity as a means of increasing production (Di Falco and Chavas 2009, Baumg¨artner and Quaas 2010). In practice though, agricultural development has relied on non- diverse systems. Using the Ethiopian Rural Household Survey panel we revisit this para- dox and disentangle the effects of agro-ecological zones and composition of crop diversity. We find a positive effect of greater cereal diversity on cereal production, but mostly in specific agro-ecological zones and for households who diversify away from a particular low- productivity crop: teff. These results indicate that the scope of cereal diversity to drive increases in output may be limited. Similar to recent studies of biodiversity—ecosystem function relationships (e.g. Jochum et al., 2020), the results suggest that the composition of diverse systems can be more important than the measured diversity itself. In the case of cereal crops in Ethiopia, differences in the yields of particular cereals in the crop mix explain the diversity effect, rather than diversity alone. Since some combinations of crops add to productivity but others do not, productivity related crop choice may not guarantee in situ conservation of crop-diversity on its own. Alternative conservation solutions may well be needed for that.

Implications of food-for-work programs for consumption and production diversity: Evidence from the Tigray Region of Ethiopia

Food-based transfer programs have the potential to change diets or alter basic crop mixes. This study empirically investigates the associations between participating in food-for-work (FFW) programs and the diversity of food consumption and production. Four waves of panel data from the Tigray Region of Northern Ethiopia, covering the period 2001–2010, are used to estimate a series of panel data regressions. A dose-response model is used to measure how the intensity of FFW participation aligns with dietary outcomes. Results show that FFW participants had greater household dietary diversity compared with non-participants, with an average magnitude equivalent to one-fifth of a standard deviation in the food variety score. When items directly provided by the FFW program are excluded from the variety score, the overall effect is statistically weaker, but similar in sign and magnitude, suggesting modest “crowding in” of dietary diversity from FFW participation. FFW participation was not correlated with changes in production diversity, suggesting that the labor demands of the program did not alter crop choice. Findings have relevance for interventions that aim to improve food security and promote dietary quality in low-income populations.