{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T18:18:56Z","timestamp":1755800336042,"version":"3.38.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:p>Soil testing can assist in determining how much fertilizer is necessary, as it depends on the fertility and crop of the soil. Through soil fertility and pH-trained hybrid architecture, a new soil nutrient prediction model for paddy agriculture is proposed in this work. First, data acquisition takes place, which is the act of gathering soil data, and it is subsequently preprocessed using the Improved Normalization method. A soil information dataset is employed in this work to help with this. Subsequently, the preprocessed data undergoes data augmentation; the correlation method facilitates an enhanced data augmentation procedure. In this case, the data used for the correlation approach is min-max normalization data. The augmented data is used to extract soil properties such as pH level and soil fertility index. Additionally, a hybrid classifier strategy that combines RNN and Modified LSTM is suggested for nutrient prediction. Lastly, this article suggested some fertilizers for nutritional insufficiency based on the projection. The hybrid prediction classifiers that have been suggested perform better in experiments than the classic classifier models, which include LSTM, RNN, SVM, Bi-GRU, and DNN, in terms of sensitivity, accuracy, FPR, MCC, precision, and efficiency in predicting nutrients. Even though the CNN (0.075), Bi-GRU (0.080), LSTM (0.087), DBN (0.078), Enhanced-1DCNN DLM (0.080), RNN (0.085), and RFA (0.052) obtained maximal FPR ratings, the FPR of the Modified LSTM+RNN scheme is 0.052.<\/jats:p>","DOI":"10.3233\/idt-240423","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T15:57:48Z","timestamp":1718726268000},"page":"685-703","source":"Crossref","is-referenced-by-count":4,"title":["Soil nutrient prediction for paddy cultivation via soil fertility and pH trained hybrid architecture: Recommendations based on nutrient deficiency"],"prefix":"10.1177","volume":"18","author":[{"given":"Kavitha","family":"S","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, G. H. Raisoni University, Amravati, Maharashtra, India"},{"name":"School of Computer Engineering, MIT Academy of Engineering, Pune, Maharashtra, India"}]},{"given":"Kotadi","family":"Chinnaiah","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, G. H. Raisoni College of Engineering, Nagpur, Maharashtra, India"}]}],"member":"179","reference":[{"issue":"7","key":"10.3233\/IDT-240423_ref1","doi-asserted-by":"crossref","first-page":"3686","DOI":"10.3390\/s23073686","article-title":"Vis-NIR spectroscopy combined with GAN data augmentation for predicting soil nutrients in degraded Alpine Meadows on the Qinghai-Tibet Plateau","volume":"23","author":"Jiang","year":"2023","journal-title":"Sensors."},{"issue":"1","key":"10.3233\/IDT-240423_ref2","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.inpa.2019.05.003","article-title":"Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters","volume":"7","author":"Suchithra","year":"2020","journal-title":"Information Processing in Agriculture."},{"issue":"1","key":"10.3233\/IDT-240423_ref3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jssas.2021.06.016","article-title":"Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods","volume":"21","author":"Pande","year":"2022","journal-title":"Journal of the Saudi Society of Agricultural Sciences."},{"issue":"2","key":"10.3233\/IDT-240423_ref4","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.iswcr.2020.12.002","article-title":"Predictions of soil and nutrient losses using a modified SWAT model in a large hilly-gully watershed of the Chinese Loess Plateau","volume":"9","author":"Shi","year":"2021","journal-title":"International Soil and Water Conservation Research."},{"issue":"7s","key":"10.3233\/IDT-240423_ref5","first-page":"1900","article-title":"Prediction of soil reaction (pH) and soil nutrients using multivariate statistics techniques for agricultural crop and soil management","volume":"29","author":"Swapna","year":"2020","journal-title":"International Journal of Advanced Science and Technology."},{"issue":"11","key":"10.3233\/IDT-240423_ref6","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.3390\/f12111430","article-title":"Prediction of regional forest soil nutrients based on Gaofen-1 remote sensing data","volume":"12","author":"Li","year":"2021","journal-title":"Forests."},{"issue":"1","key":"10.3233\/IDT-240423_ref7","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3390\/sym15010151","article-title":"Increasing the accuracy of soil nutrient prediction by improving genetic algorithm backpropagation neural networks","volume":"15","author":"Liu","year":"2023","journal-title":"Symmetry."},{"issue":"1","key":"10.3233\/IDT-240423_ref8","first-page":"91","article-title":"Soil quality prediction for determining soil fertility in Bhimtal Block of Uttarakhand (India) using machine learning","volume":"19","author":"Pant","year":"2021","journal-title":"International Journal of Analysis and Applications."},{"key":"10.3233\/IDT-240423_ref9","doi-asserted-by":"crossref","first-page":"106407","DOI":"10.1016\/j.compag.2021.106407","article-title":"A nutrient recommendation system for soil fertilization based on evolutionary computation","volume":"189","author":"Ahmed","year":"2021","journal-title":"Computers and Electronics in Agriculture."},{"key":"10.3233\/IDT-240423_ref10","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s10812-019-00891-5","article-title":"Prediction results of different modeling methods in soil nutrient concentrations based on spectral technology","volume":"86","author":"Li","year":"2019","journal-title":"Journal of Applied Spectroscopy."},{"key":"10.3233\/IDT-240423_ref11","doi-asserted-by":"crossref","unstructured":"Lelago A, Bibiso M. 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