Accurately predicting crop yield in different environmental conditions and irrigation regimes plays a vital role in optimizing agricultural practices and ensuring food security. This research aims to develop a tomato yield estimation model using machine learning (ML) models such as artificial neural network (ANN), random forest (RF), and decision tree (DT) models, based on climate and plant variables. To enhance the models' performance and prevent overfitting, a hyper-parameter tuning technique was implemented through cross-validation. Field experiments were conducted during the 2022 and 2023 growing seasons, implementing three irrigation regimes: 100%, 75%, and 50% of the full irrigation requirements (FIR). The results demonstrate that the ML models effectively captured the relationship between input variables and tomato production under deficit irrigation, achieving a desirable level of accuracy. Impressively, these models showcased predictive prowess 3-7 weeks before the harvest period. The artificial neural network models yielded an average root mean squared error (RMSE) of 3.9 ton/ha and a coefficient of determination (R2) of 0.95 for tomato yield prediction. The RF model displayed even better accuracy, with an RMSE of 3.50 ton/ha and an R2 of 0.96. The DT model forecasted tomato yield with an RMSE of 3.77 ton/ha and an R2 of 0.95. These findings highlight the practicality and reliability of utilizing climate and plant variables in combination with machine learning models to effectively manage tomato crop production, particularly when facing limited water availability for irrigation.

Major international nutrition organizations are becoming increasingly concerned about global agriculture production. In particular, food insecurity has emerged in Egypt as a result of population expansion and rising food demand. Climate change and its variability are significant factors to the worldwide food insecurity problem. Therefore, cutting-edge techniques like IOT and machine learning which have shown its worth in several ways are needed by farmers and agricultural decision-makers to assist it in making timely judgments that will affect the quality of agricultural harvests. A novel open-source technology based on Arduino Board has been developed to predict onion crop irrigation needs. By utilizing three irrigation levels (100%, 85%, and 70% ETc) and integrating machine learning, the system predicts onion output in Talkha city, Egypt for 2023/2024. Meteorological and agronomic data are combined to aid farmers and decision-makers in predicting seasonal onion yields. Our system was constructed using Artificial Neural Network (ANN), Random Forest (RF), and Decision trees (DT) models. We found that, Using Arduino device at T85% and T70% helps to rationalize amount of water by 13% and 28% comparing with T100%. Furthermore, Arduino design with algorithm model gain a good value for (CWP) by (14.5 and 16 kg/m3) at T85% and T70%. Additionally, the results indicated that the ANN model works well during the testing phase, with a R2 of 94.3%, while the RF model and DT perform 90% and 86.1%, respectively. Our results showcase the effectiveness of the technology in enhancing agricultural decision-making and crop management.

The present study was conducted to compare the reference evapotranspiration (ETo), crop coefficient (kc), applied irrigation water (AIW) and water productivity (WP) under the open field and greenhouse conditions for cucumber crops. Precise estimation of ETo, kc,ETc daily is important to apply water through a drip system in the open field and greenhouse. The ETo was estimated in the open field using “FAO-56 Penman-Monteith method” whereas, inside the greenhouse ETo was estimated using “Makkink FAO-24”. The value represents crop specific water use and it is a requirement for accurate estimation of irrigation requirements for cucumber crops. The accumulated heat units (HUs) were used to determine the kc daily for each growing stage. The results revealed that the monthly ETo values under greenhouse were less than those under open field for all months. Mean values of kc in the open field during the Four growth stage (Initial, Development, Mid and late stage) was 0.30, 0.60, 1.01 and 0.81 while it was 0.32, 0.79, 1.03 and 0.56 under greenhouse, where the Mid stage recorded the highest value in both cultivation systems. The estimated results of total AIW indicated that significant irrigation water saving occurs through the cultivation of cucumber under the greenhouse as compared to open field cultivation by nearly 33.5 % as the value of AIW inside the greenhouse was 903.9 m3/fed, while the value outside was 1359.7 m3/fed. Regarding the WP result, values were 33.46 and 15.97 kg/m3 under the greenhouse and open field conditions respectively.