Agriculture is the major factor contributing to Indian Economy. According to the current statistics, its contribution to GDP sector is 17.9%. Technical advancement in agricultural domain will produce more agricultural products without any wastage of money, time and manpower. Nutrients play a major role in plant growth. Lack of nutrients leads to reduced crop yield and plant growth. In this work, we are trying to create an artificial neural network model to recognize and classify the nutrient deficiency in tomato by examining the leaf characteristics. This will help farmers to adjust the nutrient supply to the plant. If soil lacks a specific nutrient, it will reflect in the physical characteristics of a leaf. The color and shape of a leaf are the two major features used for identifying the nutrient deficiency. The comparison of different segmentation schemes like hue based and threshold based schemes shows their influence in the performance of the proposed system. The influence of different activation functions in the artificial neural network is also studied in this work. The results show that the proposed method was able to classify and identify nutritional deficiencies with high accuracy.

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Prevalence of crop diseases is a major hindrance for successful crop production. These diseases can be identified in less time and more accurate using Machine Learning (ML) strategies as compared to any manual approach. Agronomy plays a key role in anticipating crop diseases at an early stage. With the advent of computer vision, plants can be classified as diseased or healthy by extracting architectural characteristics of a leaf using various image processing techniques. Support Vector Machines (SVM) classification technique is used in distinguishing between diseased and healthy leaf from the datasets that are publicly available. SVM method exhibited high fitting and predictive precision. The proposed paper is organized in various steps such as identifying the features, extraction of features using a computer vision technique known as Scale Invariant Feature Transform (SIFT), model training and testing. Predominantly, crop diseases on a larger scale are predicted by harmonizing speed and accuracy using computer vision and machine learning strategies.

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A very large section of the Indian population depends on farming to meet their ends. But due to several reasons - natural or man-made, not limited to weather, proper irrigation mechanisms and finance, many a section of this society are often unable to reach their expected sustenance. A proper guidance mechanism that helps the farmers or aspirants select the suitable choice of crop to be cultivated in an area based on the climatic and soil conditions will increase the agricultural productivity per unit of land. In this paper, we propose a predictive system that aids the farmers in crop selection based on different climatic and soil parameters available from a dataset pertaining to Indian subcontinent. An IOT system will determine the climatic (temperature, humidity etc) and soil (pH, moisture, etc) conditions in the area of choice. This is input to the predictive system which predicts the choice of crop. The developed system can be adopted to achieve a healthy crop growth that can be adapted by agricultural practices.