Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

An Artificial Intelligence-based Crop Recommendation System using Machine Learning


Affiliations
1 School of Engineering and Technology, Department of Computer Science and Engineering, GIET University, Gunupur 765 022, Odisha,India., India
2 CMR Technical campus, Hyderabad 501 401, Telangana, India., India
 

Agriculture is the backbone of the Indian economy and a source of employment for millions of people across the globe. The perennial problem faced by Indian farmers is that theydo not select crops based on environmental conditions, resulting in significant productivity losses. This decision support system assists in resolving this issue. In our study, the AI system helps precision agriculture improve overall crop harvest quality and accuracy. This research feature selection, Industry 4.0, proposes one solution, such as a recommendation system, using AI and a family of machine learning algorithms. The data set used in this research work is downloaded from Kaggle, and labeled. It contains a total of 08features with 07 independent variables, including N, P, K, Temperature, Humidity, pH, and rainfall. Then SMOTE data balancing technique is applied to achieve better results. Additionally, authors used optimization techniques to tune the performance further as smart factories. Cat Boosting (C-Boost) performed the best with an accuracy value of 99.5129, F-measure-0.9916, Precision-0.9918, and Kappa-0.8870. GNB, on the other hand, outperformed ROC-0.9569 and MCC-0.9569 in the classification, regression, and boosting family of machine learning algorithms.

Keywords

AI, Crop Harvesting Quality, Feature Selection, Industry 4.0, SMOTE
User
Notifications
Font Size

  • Adegbeye M J, Reddy P R, Obaisi A I, Elghandour M M, Oyebamiji K J, Salem A Z, Morakinyo-Fasipe O T, Cipriano-Salazar M & Camacho-Díaz L M, Sustainable agriculture options for production, greenhouse gasses and pollution alleviation, and nutrient recycling in emerging and transitional nations-An overview, J Cleaner Produc, 242 (2020) 18319.

  • Mutuku E A, Roobroeck D, Vanlauwe B, Boeckx P & Cornelis W M, Maize production under combined conservation agriculture and integrated soil fertility management in the sub-humidand semi-arid regions of Kenya, Field Crops Res, 254(2020) 107833.

  • KiryushinV I, The management of soil fertility and productivity of agrocenoses in adaptive-landscape farming systems, Eurasian Soil Sci, 52 (2019) 1137–1145.

  • Nordjo R E & Adjasi C K, Integrated soil fertility management (ISFM) and productivity of smallholder farmers in the northern region of Ghana (2019).

  • Ogunlade M O & Orisajo S B, Integrated soil fertility management for small holder cocoa farms: Using combination of cocoa pod husk based compost and mineral fertilizers, Int J Plant & Soil Sci, 32(2) (2020) 68–77.

  • Shukla S K, Solomon S, Sharma L, Jaiswal V P, Pathak A D & Singh P, Green technologies for improving cane sugar productivity and sustaining soil fertility in the sugarcane-based cropping system, Sugar Technol, 21(2)(2019) 186–196.

  • Nwite J C, Nwafor S O, Nwangwu A O & Olejeme O C, Enhancing soil fertility, maize grain yield, and nutrients composition through differentplanting time and manure sources in farmers' fields of Southeastern Nigeria, Asian Res J Agric,(2018) 1–12.

  • Nabavi-Pelesaraei A, Rafiee S, Mohtasebi S S, Hosseinzadeh-Bandbafha H & Chau K W, Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production, Sci Total Environ, 631(2018) 1279–1294.

  • Abrougui K, Gabsi K, Mercatoris B, Khemis C, Amami R & Chehaibi S, Prediction of organic potato yield using tillage systems and soil properties by an artificial neural network (ANN) and multiple linear regressions (MLR), Soil and Tillage Res, 190(2019) 202–208.

  • Suchithra M S & Pai M L, Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters, Inf Process Agric7(1) (2020) 72–82.

  • Kouadio L, Deo R C, Byrareddy V, Adamowski J F & Mushtaq S, Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties, Comput electron agric, 155(2018) 324–338.

  • Mohammadi T A, Ahmadi A,Gómez P A & Maghoumi M, Using the artificial neuralnetwork in determining postharvest LIFE of kiwifruit, J Sci Food Agric, 99(13) (2019) 5918–5925.

  • Toriyama K, Development of precision agriculture and ICT application thereof to manage spatial variability of crop growth, J Soil Sci Plant Nutr, 66(6) (2020) 811–819.

  • Bestelmeyer B T, Marcillo G, McCord S E, Mirsky S, Moglen G, Neven L G, PetersD, Sohoulande C &Wakie T, Scaling up agricultural research with artificial intelligence, IT Prof, 22(3) (2020) 33–38.

  • Dharmaraj V & Vijayanand C, Artificial intelligence (AI) in agriculture, Int J Curr Microbiol Appl Sci, 7(12)(2018) 2122–2128.

  • Palanivel K & Surianarayanan C, An approach for prediction of crop yield using machine learning and big data techniques, Int J Comput Eng Technol, 10(3)(2019) 110–118.

  • Apat S K, Mishra J, Raju K S & Padhy N, A study on smart agriculture using various sensors and agrobot: A case study, Smart Intel Comput Appl, 1(2022) 531–540.

  • Dasgupta A, Drineas P, Harb B, Josifovski V & Mahoney M W, Feature selection methods for text classification, Proc 13 th ACM SIGKDD Int conf Knowl Discov Data Mining, 2007, 230–239.

  • Kou G, Yang P, Peng Y, Xiao F, Chen Y & Alsaadi F E, Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods, Appl Soft Comput, 86(2020) 105836.

  • Pandith V, Kour H, Singh S,Manhas J S & Sharma V, Performance evaluation of machine learning techniques for mustard crop yield prediction from soil analysis, J Sci Res 64(2)(2020) 394–398.

  • Maya Gopal P S & Bhargavi R, Performance evaluation of best feature subsets for crop yield prediction using machine learning algorithms, Appl Artif Intell, 33(7)(2019) 621–642.

  • Blackman N J & Koval J J, Interval estimation for Cohen's Kappa as a measure of agreement, Stat Med,19(5)(2000) 723–741.

  • Apat S K, Mishra J, Raju K S & Padhy N, The robust and efficient Machine learning model for smart farming decisions and allied intelligent agriculture decisions, Int J Integr Sci Technol, 10(2)(2022) 139–155.

  • Argüeso D, Picon A, Irusta U, Medela A, San-Emeterio M G, Bereciartua A & Alvarez-Gila A,few-shot learning approach for plant disease classification using images taken in the field, Comput Electron Agric,175(2020) 105542.

  • Apat S Kumar, Mishra J, Raju K S & Padhy N, IoT-assisted crop monitoring using machine learning algorithms for smart farming, in Next Generation of Internet of Things. Lecture Notes inNetworks and Systems (Springer, Singapore). edited byR Kumar, P K Pattnaik, R S, J M Tavares, 445, https://doi.org/10.1007/978-981-19-1412-6_1

  • Toseef M & Khan M J, An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system, Comput Electron Agric, 153(2018) 1–11.

  • Muangprathub J, Boonnam N, Kajornkasirat S, Lekbangpong N, Wanichsombat A &Nillaor P, IoT and agriculture data analysis for smart farm, Comput Electron Agric, 156(2019) 467–474.

  • Reynolds M, Kropff M, Crossa J, Koo J, Kruseman G, Molero Milan A, Rutkoski J, Schulthess U, Sonder K, Tonnang H & Vadez V, Role of modeling in international crop research: overview and some case studies, Agronomy, 8(12)(2018) 291.

  • Apat S K, Mishra J, Srujan Raju K & Padhy N, State of the art of ensemble learning approach for crop prediction, Next Gen IoT, (2023) 675–685.

  • Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J & Johannes A, Deep convolutional neural networks for mobile capturedevice-based crop disease classification in the wild, Comput Electron Agric,161(2019) 280–290.


Abstract Views: 115

PDF Views: 95




  • An Artificial Intelligence-based Crop Recommendation System using Machine Learning

Abstract Views: 115  |  PDF Views: 95

Authors

Shraban Kumar Apat
School of Engineering and Technology, Department of Computer Science and Engineering, GIET University, Gunupur 765 022, Odisha,India., India
Jyotirmaya Mishra
School of Engineering and Technology, Department of Computer Science and Engineering, GIET University, Gunupur 765 022, Odisha,India., India
K Srujan Raju
CMR Technical campus, Hyderabad 501 401, Telangana, India., India
Neelamadhab Padhy
School of Engineering and Technology, Department of Computer Science and Engineering, GIET University, Gunupur 765 022, Odisha,India., India

Abstract


Agriculture is the backbone of the Indian economy and a source of employment for millions of people across the globe. The perennial problem faced by Indian farmers is that theydo not select crops based on environmental conditions, resulting in significant productivity losses. This decision support system assists in resolving this issue. In our study, the AI system helps precision agriculture improve overall crop harvest quality and accuracy. This research feature selection, Industry 4.0, proposes one solution, such as a recommendation system, using AI and a family of machine learning algorithms. The data set used in this research work is downloaded from Kaggle, and labeled. It contains a total of 08features with 07 independent variables, including N, P, K, Temperature, Humidity, pH, and rainfall. Then SMOTE data balancing technique is applied to achieve better results. Additionally, authors used optimization techniques to tune the performance further as smart factories. Cat Boosting (C-Boost) performed the best with an accuracy value of 99.5129, F-measure-0.9916, Precision-0.9918, and Kappa-0.8870. GNB, on the other hand, outperformed ROC-0.9569 and MCC-0.9569 in the classification, regression, and boosting family of machine learning algorithms.

Keywords


AI, Crop Harvesting Quality, Feature Selection, Industry 4.0, SMOTE

References