Browsing by Author "Harsányi, Endre"

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Data Mining and Machine Learning Algorithms for Optimizing Maize Yield Forecasting in Central Europe
(MDPI, 2023-05) Harsányi, Endre; Bashir, Bashar; Arshad, Sana; Ocwa, Akasairi; Vad, Attila; Alsalman, Abdullah; Mohammed, Safwan; Széles, Adrienn; Hijazi, Omar; Rátonyi, Tamás; Bácskai, István
Artificial intelligence, specifically machine learning (ML), serves as a valuable tool for decision support in crop management under ongoing climate change. However, ML implementation to predict maize yield is still limited in Central Europe, especially in Hungary. In this context, we assessed the performance of four ML algorithms (Bagging (BG), Decision Table (DT), Random Forest (RF) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP)) in predicting maize yield based on four different input scenarios. The collected data included both agricultural data (production (PROD) (ton) and maize cropped area (AREA) (ha)) and climate data (annual mean temperature C (Tmean), precipitation (PRCP) (mm), rainy days (RD), frosty days (FD) and hot days (HD)). This research adopted four scenarios, as follows: SC1: AREA+ PROD+ Tmean+ PRCP+ RD+ FD+ HD; SC2: AREA+ PROD; SC3: Tmean+ PRCP+ RD+ FD+ HD; and SC4: AREA+ PROD+ Tmean+ PRCP. In the training stage, ANN-MLP-SC1 and ANN-MLP-SC4 outperformed other ML algorithms; the correlation coefficient (r) was 0.99 for both, while the root mean squared errors (RMSEs) were 107.9 (ANN-MLP-SC1) and 110.7 (ANN-MLP-SC4). In the testing phase, the ANN-MLP-SC4 had the highest r value (0.96), followed by ANN-MLP-SC1 (0.94) and RF-SC2 (0.94). The 10-fold cross validation also revealed that the ANN-MLP-SC4 and ANN-MLP-SC1 have the highest performance. We further evaluated the performance of the ANN-MLP-SC4 in predicting maize yield on a regional scale (Budapest). The ANN-MLP-SC4 succeeded in reaching a high-performance standard (r = 0.98, relative absolute error = 21.87%, root relative squared error = 20.4399% and RMSE = 423.23). This research promotes the use ofANNas an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management. Keywords: maize yield; climate; multilayer perceptron; random forest; optimum model
Maize Grain Yield and Quality Improvement Through Biostimulant Application: a Systematic Review
(Springer, 2024-02) Ocwa, Akasairi; Mohammed, Safwan; Mousavi, Seyed Mohammad Nasir; Illés, Árpád; Bojtor, Csaba; Ragán, Péter; Rátonyi, Tamás; Harsányi, Endre
Increasing the productivity of cereals such as maize while protecting the environment remains a fundamental impetus of healthy food production systems. The use of biostimulants is one of the sustainable strategies to achieve this balance, although the ability of biostimulants to enhance maize productivity varies. Moreover, research on the efficacy of biostimulants is ubiquitous with limited comprehensive global analysis. In this context, this systematic review evaluated the sole and interactive effects of biostimulants on the yield and quality of maize grain from a global perspective. Changes in yield (t ha-1), protein content (%), starch content (%) and oil content (%) of maize grain were assessed. Results revealed that sole and combined application of biostimulants significantly improved grain yield. Irrespective of the region, the highest and the lowest grain yields ranged between 16-20 t ha-1 and 1-5 t ha-1, respectively. In sole application, the promising biostimulants were chicken feather (16.5 t ha-1), and endophyte Colletotrichum tofieldiae (14.5 t ha-1). Sewage sludge × NPK (15.4 t ha-1), humic acid × control release urea (12.4 t ha-1), Azospirillum brasilense or Bradyrhizobium japonicum × maize hybrids (11.6 t ha- 1), and Rhizophagus intraradices × earthworms (10.0 t ha- 1) had higher yield for the interactive effects. The effects of biostimulants on grain quality were minimal, and all attributes improved in the range from 0.1 to 3.7%. Overall, biostimulants had a distinct improvement effect on yield, rather than on the quality of grain. As one way of maximising maize productivity, soil health, and the overall functioning of crop agroecosystems, the integrated application of synergistic microbial and non-microbial biostimulants could provide a viable option. However, the ability to produce consistent yield and quality of grain improvement remains a major concern.
Seed treatment with Bacillus bacteria improves maize production: a narrative review
(OJS, 2024-06) Ocwa, Akasairi; Ssemugenze, Brian; Harsányi, Endre
Maize (Zea mays L.) is an important crop in relation to its production and consumption. Production of maize is constrained by soil infertility and poor quality seed. Microbial technologies like seed treatment with Bacillus bacteria improves the productivity of maize on infertile soil. However, due to variations in maize growth environments and Bacillus species, this review was conducted to identify the common species of Bacillus species used for seed treatment, and provide an overview of the effect of seed treatment with Bacillus on maize growth and yield. Results show that Bacillus subtilis, Bacillus pumilus and Bacillus amyloliquefaciens were the dominant species used for seed treatment. Bacillus was used as both a biofertiliser and biopesticide. The conspicuous positive effects of Bacillus were in plant height, shoot and root length, and shoot dry matter depending on the species. In terms of grain yield, Bacillus subtilis (8502 kg ha-1), Bacillus amyloliquefaciens (6822 kg ha-1) and Bacillus safensis (5562 kg ha-1) were the bacterial species that had an overall pronounced effect. The highest increase in grain yield was in the interactive effect of Bacillus megaterium + Bacillus licheniformis (18.1%) and sole Bacillus subtilis (15.6%), while Bacillus pumilus reduced grain yield by 4.8%. This shows that the improvement of maize productivity using Bacillus bacteria requires careful selection of the species for seed treatment. Keywords: Bacillus bacteria; biopriming; maize; seed treatment; yield