International Journal of Agrochemistry
Predictive Farming Using Image Based Machine Learning
Abstract
Agriculture sector is considered as central pillar of Indian economics contributing approximately18% of total GDP employing almost 78% of rural population. Export of agro- based commodities hasincreased by 7-folds in past 1.5 decade. Therefore, it is necessary to uplift quality of agro-commoditiesproductions. Improving crop quality and quantity by sowing appropriate crop according to soil typeand weather condition become an hour’s need today. There are variety of soil series available in Indiaand generally, cultivation of crops is specific to soil, climate, nutrients, and amount of waterrequirement. Another most concerned factor is plant disease deciding crop’s quality and quantity; itestimated that every year up to 40% of crop yield worth 5 million crores of rupees is lost due to suchdisease condition, pests, or weeds. Identification of such plant diseases and categorizing them insystematic way becomes essential today. Principal problem associated with identification andcategorization of plant diseases is incapable human intervention on vast agro-fields which resulted inunnoticed onset of plant disease. Even a single infected plant is capable of spreading the disease toother plants on field; therefore, prompt treatment is required in order to save quality of crop. The givenproblems encouraged to develop machine learning system which not only helps to predict the newercrops as per soil type may be in between two major crops to maintain the soil fertility but alsoaccurately detect the onset of disease spreading over plant. A model developed for classifying soilseries and providing suitable crop suggestion has been tested by applying different kinds of machinelearning algorithms, particularly image processing using CNN algorithm gained expertise acceptancelevel thus encouraged to try this new technology. The output images shown in the results indicate thatwith image processing, CNN algorithm can accurately provide a crop suggestion and early diseaseidentification.References (9)
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