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The growing global challenges of environmental degradation and resource scarcity demand innovative agricultural solutions. Intelligent environmental control systems integrating sensors, automation, and artificial intelligence (AI) optimize crop production and sustainability in vertical farming. This review explores the critical role of these technologies in monitoring and adjusting key environmental parameters, including light, temperature, humidity, nutrient delivery, and CO₂ enrichment. Intelligent environmental control systems use real‐time data from sensor networks to continuously maintain optimal growing conditions. Sensors measure changes in the environment, such as light intensity and humidity levels. Automation enables tasks to be performed without human intervention, ensuring consistent adjustments to environmental conditions. AI predicts plant responses and enables proactive management strategies in this context. The review also examines how these technologies integrate, highlighting successful case studies and addressing challenges like energy management, scalability, and system harmonization. Looking ahead, AI's potential in predictive maintenance and emerging trends in vertical farming highlight the transformative role of intelligent environmental control in enhancing agricultural efficiency and sustainability.

Effective irrigation management is crucial for optimizing crop production, particularly in water‐scarce regions. This study evaluated the performance of an Arduino‐based system designed to monitor and control soil moisture in a greenhouse setting, focusing on its impact on tomato plant growth, fruit yield, and fruit size under two different irrigation treatments. Treatment 1 (T1) involved low moisture with significant fluctuations (55%–85% soil moisture), while Treatment 2 (T2) maintained optimal and stable moisture levels (70%–85%). Soil moisture dynamics revealed that in T1, moisture levels oscillated significantly, dropping to 55% before irrigation restored them to 85%. This cyclical pattern indicates a stress‐response mechanism triggered by the system, which is essential for mitigating plant stress and ensuring optimal growth. Conversely, the optimal moisture treatment maintained more stable soil moisture levels between 70% and 85%, promoting healthy plant development and physiological functions. The correlation between sensor readings and gravimetric measurements was analyzed using a 45° diagonal correlation approach, demonstrating strong agreement between the two methods and reinforcing the reliability of sensor‐based irrigation. Physiological assessments indicated that seedlings under optimal irrigation experienced a 30% increase in fresh weight, a 6% increase in dry weight, a 16% increase in plant height, and a 25% higher SPAD values compared to T1 at the young stage. At maturity, T2 plants exhibited a 52% increase in fresh weight, a 78% increase in dry weight, and a 121% increase in plant height. Fruit yield increased by 47% in T2, with an average of 56 fruits per plant compared to 45 in T1, and the average fruit weight was 85 g in T2 compared to 56 g in T1. Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. By refining these technologies, agriculture can achieve more sustainable and productive outcomes in the face of increasing environmental challenges.

This study aimed to optimize water productivity and wheat yield in the rainfed wheat systems of the Honam plain, a critical region in the upper Karkheh River basin of Iran. In the first two years of research (2013–2014 and 2014–2015), the prevailing status of the region was investigated with regards to wheat yield and rainfall productivity under rainfed conditions. Thereafter, different management scenarios were defined and investigated to improve wheat yield, rainfall productivity, and water productivity. In the second year of research (2014–2015), the best management scenarios selected from the first two years were tested in some selected rainfed wheat farms in the Honam plain. The results showed that wheat biomass and grain yields from these best scenarios under rainfed and single irrigation (SI) conditions could be accurately predicted using the AquaCrop model. At the model validation stage, the RMSE was 0.16 for grain yield and 0.32 ton ha −1 for biomass and the NRMSE was 5 and 4%, respectively. Whether for grain yield or crop biomass, the coefficient of determination was about 0.86. The proposed scenarios for AquaCrop modelling were then trialed for rainfed wheat and showed better agronomic advantages than the traditional crop management practices. By applying a single irrigation in spring, the mean total water productivity (rainfall + irrigation) for wheat increased to 0.70 kg m −3 , being 74% higher than that under rainfed conditions. The best management plan in the Honam plain was the combination of superior crop management with single irrigation in spring (60 mm) during the mid-flowering period, which increased the grain yield by 176% and rainfall productivity by 134%. The results from this management scenario were satisfactorily simulated by the AquaCrop model.

The monitoring of water requirement in irrigation areas is mostly performed by on-farm methods like utilization of soil probes, tensiometers, or neutron probes. The probes are placed into the soil collected from different depths of the root zone of the crop. But such procedures are found to be time-consuming. As a result, non-portable capacitance-based probes were nowadays utilized for monitoring of soil moisture. However, the sensor-based non-portable system is expensive and out of reach of ordinary farmers. But an absence of on-time monitoring of soil moisture in the root zone of the soil often results in crop failure and incurs a substantial loss on the cultivators. In the present investigation, a real-time inexpensive water monitoring system was proposed to monitor soil moisture in the root zone of a crop such that both time and expenditure can be reduced. The present study is an attempt to develop a real-time monitoring process for crop water requirement (CWR) in protected farm irrigation systems as a function of the significant parameters such as soil porosity (SP), water availability, crop biomass equivalent (CBE), frequency of nutrient application, frequency of irrigation, and CWR. A systematic literature review was performed to identify parameters for CWR, which were then selected by a relevant group of experts on the field. A two-step methodology was followed to develop a function that can automatically estimate water requirement in the root zone of the crop. In the first step, a new probability optimization technique (POT) was proposed for the identification of the priority value of the selected parameters to generate an ideal scenario. In the second step, the index, developed from the parameters and respective priorities selected in the first step, was predicted recurring to polynomial neural network models. The implementation of the nonlinear transfer function in the development of the neural network framework ensures generation of a platform-independent model, which can be embedded to monitor watering requirement for crops cultivated in a protected farm concept. The data of SP and CBE were retrieved from two separate indices (index of soil porosity and biomass index) calculated from images captured from the root and surface areas of the crops. Here, the POT method was used followed by the z score of priority function of the selected parameters estimated by polynomial networks and was fed for the calculation of the water requirement index (WRI). The normalized relative difference of the WRI of two consecutive days provides the information about the necessity of watering and accordingly, the crops in the system are irrigated. The results from the decision-making method indicated that the most significant parameter among the compared factors is CWR. The peak pixel value of each column of the image, for retrieving information from captured images and to identify soil porosity and biomass, was found to be the most contributing factor. The polynomial neural network (PNN) model trained with the information from POT method was found to be the best predictive variant among all the considered configuration of the model having a mean absolute accuracy of 99.08% during the testing phase of the PNN model. This real-time system, when implemented in a real-life scenario, can conserve both water and energy expended in running the watering networks of protected farms.

A field experiment conduct during winter season 2022-2023. This study was aimed to investigate effect number of seeds.m2 to two wheat varieties. The results showed the superiority of  V1 in most main stem growth traits (biological yield, number of ears .In square meters, plant height, spike length, spikelets number / ear and chlorophyll content ( 1.87 t. ha, 20.0 seed. m2 , 113.90 cm , 18.40 cm, 26.37 spikelets/ ear  and 75.92 (SPAD ) respectively. V1 also excelled in all the traits of branch growth ( biological yield, number of peduncles /m2, plant height, spike length and spikelets number / ear, (16.71 tons .Ha-1, 259.17 ear /m2  ,104.57 cm, 16.83 cm and 23.63 spikelets / ear ) respectively . comparative to V2 . for the seeding rates per square meter; S2 produced the highest main stem growth traits of biological yield, plant height and chlorophyll content, (16.71 ton. ha-1 , 103.63 cm and 83.99 SPAD) respectively .in compare to (S1 and S3 seeding rates), even more; S2 produced the highest biological yield in tillers No. (14.56 ton .ha-1) which was significantly  higher than seeding rates ((S1 and S3) ], while S3 produced the highest ears number. (265.00 ear.m2) compared to other seeding rates.

Agriculture is the crucial pillar and basic building block of our nation. Agriculture plays a key role as the major source of revenue for our nation. Farming is the primary financial source of India. Abrupt environmental changes affect crop yield prediction. Unpredictable climate changes, lack of water resources, deficiency of nutrients, depletion of soil fertility, unbalanced irrigation systems, and conventional farming techniques are the major causes of crop yield prediction. Today, AI, the use of machine learning, and deep learning techniques provide an achievable solution to improve crop yields. The key intent of the survey is to accurately predict and improve crop yield by combining agricultural statistics with machine learning and deep learning models. To accomplish this, we have surveyed the optimization algorithms implemented in conjunction with the Random Forest and Cat Boost models. A survey made across multiple databases to determine the effectiveness of crop yield prediction and analysis was performed on the included articles. The survey results show that a hybrid CNN DNN and RNN model with optimization algorithms outperforms the other existing traditional models.

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms.

The practice of cover crops has gained popularity as a strategy to improve agricultural sustainability, but its full potential is often limited by environmental trade-offs. Using meta-analytic and data-driven quantifications of 2302 observations, we optimized cover crop practices and evaluated their benefits for global agroecosystems. Cover crops have historically boosted crop yields, soil carbon storage, and stability, but also stimulated greenhouse gas emissions. However, combining them with long-term implementation (five years or more) and climate-smart practices (such as no-tillage) can enhance these services synergistically. A biculture of legume and non-legume cover crops, terminated 25 days before planting the next crop and followed by residue mulching, is the optimal portfolio. Such optimized practices are projected to increase agroecosystem multiservices by 1.25%, equivalent to annual gains of 97.7 million metric tons in crop production, 21.7 billion metric tons in carbon dioxide sequestration, and 2.41 billion metric tons in soil erosion reduction. By 2100, the continued implementation of optimized practices could mitigate climate-related yield losses and contribute to climate neutrality and soil stabilization, especially in harsh and underdeveloped areas. These findings underscore the promising potential of optimized cover crop practices to achieve the synergy in food security and environmental protection. Cover crops can improve agricultural sustainability. In this meta-analysis, the authors find that a biculture of legume and non-legume cover crops is optimal and may promote multiple agroecosystem services while mitigating climate-related yield losses by 2100.

The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Advances in deep learning have greatly enhanced disease diagnostic techniques in agriculture. Accurate identification of rice plant diseases is crucial to preventing the severe consequences these diseases can have on crop yield. Current methods often struggle with reliably diagnosing conditions and detecting issues in leaf images. Previously, leaf segmentation posed challenges, and while analyzing complex disease stages can be effective, it is computationally intensive. Therefore, segmentation methods need to be more accurate, cost-effective, and reliable. To address these challenges, we propose a hybrid bio-inspired algorithm, named the Hybrid WOA_APSO algorithm, which merges Adaptive Particle Swarm Optimization (APSO) with the Whale Optimization Algorithm (WOA). For disease classification in rice crops, we utilize a Convolutional Neural Network (CNN). Multiple experiments are conducted to evaluate the performance of the proposed model using benchmark datasets (Plantvillage), with a focus on feature extraction, segmentation, and preprocessing. Optimizing feature selection is a critical factor in enhancing the classification algorithm’s accuracy. We compare the accuracy, sensitivity, and specificity of our model against industry-standard techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and conventional CNN models. The experimental results indicate that the proposed hybrid approach achieves an impressive accuracy of 97.5% (Refer Table 8), which could inspire further research in this field.

Background As identified by the research, it is imperative to develop effective ways to address the pressing problem of disease and pest susceptibility in chili agriculture and secure sustainable crop yield. The research examines the impact of various growing microclimates, watering regimens, and chili cultivars on disease incidence, pest attacks, and yield loss. Results The study, which took place over a season, used a randomized complete block design to evaluate how well Tanjung, Unpad, and Osaka cultivars performed in four different watering regimens (100, 75, 50, and 25% ETc) and different microclimates (greenhouse, rain shelter, screen house, and open field). The findings exhibited that watering regimens and microclimates greatly influenced disease and pest occurrence, but cultivars had a minimal effect on these variables. Disease and pest attack rates were highest in the open field and lowest in the screen house. A correlation was found between lower disease and pest incidence and optimal irrigation levels (75% and 100% ETc). At lower watering regimens of 25% ETc and in the open field, yield loss was the greatest. Conclusion The results emphasize how crucial controlled environments and appropriate irrigation techniques are to reducing crop loss and increasing production. Enhancing watering regimens and implementing screen house cultivation are two strategies for improving the productivity and sustainability of chili output.