Enhancing AI based evaluation for smart cultivation and crop testing using agro-datasets

Shibin David

1 Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
Email: [email protected]
2 Department of mechanical engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
 Email: [email protected]
3 Department of Electronics and Communication Engineering, Coimbatore, India
Email: [email protected]
*Corresponding Author: Shibin David, Email: [email protected]

In India, Agriculture is considered as the backbone of the country where 70 percent of the economy relies upon it. It requires the involvement of many natural resources including land, water, and energy. An astonishing factor is that 60 percent of the water diverted or pumped for irrigation is wasted via runoff into waterways or evapotranspiration. Although chemical fertilizers improve the growth of plants and increase the yields of fruits and vegetables in a relatively short period of time, there are certain shortcomings of using chemical fertilizers as an opponent to the use of organic fertilizers obtained from natural wealth. The persistent use of chemical fertilizers causes the pollution of ground water sources, or leaching. Since the chemicals present in the chemical fertilizers spoil the soil scraps, the inference of this will be a high impact on the soil with reduced drainage and in the air circulation. The synthesizers that are used for farming will adversely affect the nature and the pH of the soil. Nowadays water scarcity is considered to be major concern in the cultivation process. Alongside, yet another major problem faced in cultivation is the usage of lots of fertilizers which makes the land infertile.. In this paper, the nature of the work to sense various factors such as atmospheric temperature, soil moisture, rain, and pH value through a GSM module. Using the aforementioned factors, the farmers and landlords can able to predict how much water is required for the land and how much nutrients required for land. The technological advancement in artificial intelligence paves a way to detect the sensed values and predict whether a crop could be planted on the soil present in a region.

Arduino UNO, Soil moisture sensor, pH sensor, Temperature sensor, Rain drop sensor, GSM module, Agriculture land

Shibin David, R.S. Anand, Martin Sagayam (2020). Enhancing AI based evaluation for smart cultivation and crop testing using agro-datasets. Journal of Artificial Intelligence and Systems, 2, 149–167. https://doi.org/10.33969/AIS.2020.21010.

[1] Parameswaran, G. and Sivaprasath, K. (2016) Arduino based smart drip irrigation system using Internet of Things. International Journal of Engineering Science and Computing, 5518.
[2] Angel, C., & Asha, S. (2015). A study on developing a smart environment in agricultural irrigation technique. International Journal of Ambient Systems and Applications (IJASA), 3, 11-17.
[3] Jagannathan, S., & Priyatharshini, R. (2015). Smart farming system using sensors for agricultural task automation. In 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR) IEEE. 49-53.
[4] Devika, S. V., Khamuruddeen, S., Khamurunnisa, S., Thota, J., & Shaik, K. (2014). Arduino based automatic plant watering system. International Journal of Advanced Research in Computer Science and Software Engineering, 4(10), 449-456.
[5] Laursen, M. S., Midtiby, H. S., Krüger, N., & Jørgensen, R. N. (2014). Statistics-based segmentation using a continuous-scale naive Bayes approach. Computers and electronics in agriculture, 109, 271-277.
[6] Hadi, W. E., Al-Radaideh, Q. A., & Alhawari, S. (2018). Integrating associative rule-based classification with Naïve Bayes for text classification. Applied Soft Computing, 69, 344-356.
[7] Seka, D., Bonny, B. S., Yoboué, A. N., Sié, S. R., & Adopo-Gourène, B. A. (2019). Identification of maize (Zea mays L.) progeny genotypes based on two probabilistic approaches: Logistic regression and naïve Bayes. Artificial Intelligence in Agriculture, 1, 9-13.
[8] Mukhopadhyay, S., Chakraborty, S., Bhadoria, P. B. S., Li, B., & Weindorf, D. C. (2020). Assessment of heavy metal and soil organic carbon by portable X-ray fluorescence spectrometry and NixPro™ sensor in landfill soils of India. Geoderma Regional, 20, e00249.
[9] Deng, F., Zuo, P., Wen, K., & Wu, X. (2020). Novel soil environment monitoring system based on RFID sensor and LoRa. Computers and Electronics in Agriculture, 169, 105169.
[10] Kassaye, K. T., Boulange, J., Saito, H., & Watanabe, H. (2020). Monitoring soil water content for decision supporting in agricultural water management based on critical threshold values adopted for Andosol in the temperate monsoon climate. Agricultural Water Management, 229, 105930.
[11] Zhou, W., Xu, Z., Ross, D., Dignan, J., Fan, Y., Huang, Y., & Li, B. (2019). Towards water-saving irrigation methodology: Field test of soil moisture profiling using flat thin mm-sized soil moisture sensors (MSMSs). Sensors and Actuators B: Chemical, 298, 126857
[12] Domínguez-Niño, J. M., Oliver-Manera, J., Girona, J., & Casadesús, J. (2020). Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors. Agricultural Water Management, 228, 105880.
[13] Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Rossel, R. V., Terribile, F., & Venezia, A. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture. Catena, 167, 293-304.
[14] Leue, M., Hoffmann, C., Hierold, W., & Sommer, M. (2019). In-situ multi-sensor characterization of soil cores along an erosion-deposition gradient. Catena, 182, 104140.
[15] Radovanović, M., Vasiljević, D., Krstić, D., Antić, I., Korzhyk, O., Stojanović, G., & Škrbić, B. D. (2019). Flexible sensors platform for determination of cadmium concentration in soil samples. Computers and Electronics in Agriculture, 166, 105001.
[16] Roy, S. K., & De, D. (2020). Genetic Algorithm based Internet of Precision Agricultural Things (IopaT) for Agriculture 4.0. Internet of Things, 100201.
[17] Corani, G., & Zaffalon, M. (2008). JNCC2: An extension of naive Bayes classifier suited for small and incomplete data sets. Environmental Modelling and Software, 23(7), 960-961.
[18] Barca, E., Castrignanò, A., Ruggieri, S., & Rinaldi, M. (2020). A new supervised classifier exploiting spectral-spatial information in the Bayesian framework. International Journal of Applied Earth Observation and Geoinformation, 86, 101990.
[19] Bargiel, D. (2017). A new method for crop classification combining time series of radar images and crop phenology information. Remote sensing of environment, 198, 369-383.
[20] Diengdoh, V. L., Ondei, S., Hunt, M., & Brook, B. W. (2020). A validated ensemble method for multinomial land-cover classification. Ecological Informatics, 56, 101065.
[21] Ip, R. H., Ang, L. M., Seng, K. P., Broster, J. C., & Pratley, J. E. (2018). Big data and machine learning for crop protection. Computers and Electronics in Agriculture, 151, 376-383.
[22] Ramya, M. C., Lokesh, V., Manjunath, T. N., & Hegadid, R. S. (2015). A predictive model construction for mulberry crop productivity. Procedia Computer Science, 45, 156-165.
[23] Keswani, B., Mohapatra, A.G., Mohanty, A. et al. Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Comput & Applic 31, 277–292 (2019). 
[24] Jatin Arora, Utkarsh Agrawal, and Prerna Sharma (2020). Classification of Maize leaf diseases from healthy leaves using Deep Forest. Journal of Artificial Intelligence and Systems, 2, 14–26.
[25] Deepak Gupta, Prerna Sharma, Krishna Choudhary, Kshitij Gupta, Rahul Chawla,Ashish Khanna, Victor Hugo C. de Albuquerque (2020), "Artificial plant optimization algorithm to detect infected leaves using machine learning", Epert Systems.
[26] Aditya Khamparia, Gurinder Saini, Deepak Gupta, Ashish Khanna, Shrasti Tiwari, Victor Hugo C. de Albuquerque (2019), "Seasonal crop disease prediction and classification using Deep Convolutional Encoder network", Circuits, Systems, and Signal Processing.