Manual Harvesting Research Articles

Autonomous Self-Propelled Napa Cabbage Harvester: Cutting, Attitude Control, and Loading Modules

This paper introduces an autonomous self-propelled Napa cabbage harvester, designed to significantly improve the efficiency and effectiveness of the traditionally labor-intensive harvesting process. The harvester integrates three key modules: a cutting, an attitude control, and a loading module. The cutting module is equipped with an attitude control module that ensures precise severance of the Napa cabbage stems, minimizing damage to the crop and maintaining product quality. The attitude control module employs a backstepping-based force control that continuously adjusts the cutting angle and height to ensure consistent cutting precision, even on uneven terrain, thereby optimizing the quality of the Napa cabbages. The loading module automates the collection and transfer of harvested Napa cabbages into storage, significantly reducing the physical burden on workers and improving operational efficiency. Field experiments demonstrated improvements, including a 42–66% reduction in task time compared to manual harvesting, as well as a 37% increase in cutting accuracy through the use of autonomous control. The proposed system presents a comprehensive solution for enhancing productivity, reducing labor demands, and maintaining high crop quality in Napa cabbage harvesting, offering a practical approach to modernizing agricultural practices.

Stepwise methods for more nuanced adoption analysis: a case study of harvest and post-harvest mechanization in Bangladesh

The adoption of agricultural harvest and post-harvest mechanization is crucial for addressing drudgery, food losses, climate vulnerability and food security. Despite considerable efforts by government and development partners to prioritize agricultural mechanization, labour-intensive manual (post-)harvest activities continue to dominate in Bangladeshi smallholder systems. Explorations of this has been limited by simplistic binary approaches that ignore the dynamic pathways to usage outcomes. Instead, we apply non-binary analytical methods to district representative data to highlight the value in moving beyond binary adoption analysis. Results highlight that a national (post-)harvest mechanisation rate of 74% is insufficient to capture the true adoption status, given substantial disparity exists across machinery and by district. Deeper exploration of temporal and spatial differences enable the identification of key trends that warrant further in-depth explorations, while only 46% satisfaction with extension systems highlights the need to re-evaluate key information exchange mechanisms. We conclude that there is a clear need for district and machinery specific policy arrangements if Bangladesh is to achieve (post-)harvest mechanisation objectives that aim to reduce food loses and enable greater food security nationwide.

Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions

The global demand for button mushrooms has surged in recent years, driven by their health benefits, creating a significant challenge for the mushroom industry in meeting this increasing demand. The increasing reliance on human labor, which is becoming unsustainable due to labor shortages and rising wage costs, highlights the urgent need for automated harvesting solutions. This review examines the integration of automated systems in button mushroom harvesting, delving into the key components such as robots, mechanisms, machine elements, programming, and algorithms. It offers a thorough analysis of the performance, design, operational mechanisms, and advantages and limitations of robotic systems, comparing the different methods employed in automated harvesting. This paper compares the performance of all the mushroom harvesters, including the commercially available ones with manual harvesting, and identifies their potential and limitations. The commercial harvesters are shown to pick 2000 mushrooms per hour on average, which is similar to how much a skilled worker picks at the same time. However, commercial automation harvesting has a relatively low success rate, high initial cost, high operating cost, and energy consumption, identifying areas for future research and challenges. This paper serves as a valuable resource for researchers and industry professionals striving to advance automated harvesting technology and improve its efficiency in meeting the rising demand for button mushrooms.

Performance Evaluation of Groundnut Digger in Chhattisgarh, India

Groundnut (Arachis hypogaea L.), is a significant oilseed crop. It is grown in Chhattisgarh during rabi and kharif seasons, with traditional manual harvesting being labor-intensive and costly. To enhance efficiency, a study evaluated a tractor-operated groundnut digger in Chhattisgarh, focusing on factors such as crop size, shape, depth, spacing, and soil parameters like bulk density, true density, and moisture content. Performance metrics included exposed pod loss, buried pod loss, damaged pod loss, total pod loss, field efficiency, and digging efficiency. In this study found that at a 30° rake angle and 3.5 km h-1 speed, the maximum buried pod loss was 7.37%, exposed pod loss was 9.89%, and total pod loss was 23.79%. Conversely, the digger achieved a high digging efficiency of 96.92% with a 25° rake angle and 2 km h-1 speed. The operational cost was 972.57 Rs h-1, with a breakeven point of 100.42 hours per year and a payback period of 0.91 years. Compared to manual harvesting, which costs 37.5 Rs h-1, the tractor-operated digger significantly reduces time and costs, offering substantial improvements in harvesting efficiency.

Evaluation of Tractor Mounted Axial Flow Combine Harvester for Mung Bean (Vigna radiata L.)

A cost-effective, tractor mounted axial flow combine harvester designed to minimize harvesting losses was evaluated. Performance evaluation of the machine was done on summer mung bean (Vigna radiata L. Wilczek) under various operational conditions: three forward speeds (FS): 2.1, 3.0, and 4.8 km h-1, three-cylinder peripheral speeds (PS): 11.08, 13.16, and 14.79 m s-1, and days after irrigation (DAI): 2, 4, and 6 days after last irrigation. Threshing efficiency, cleaning efficiency, broken percentage, shattering losses, and non-collectable losses were estimated. Cost of operation of the combine harvester was compared with manual harvesting and subsequent threshing using a spike tooth type thresher. The results show that the threshing efficiency and grain breakage increased by 1.1% and 1.01%, respectively, with an increase in DAI and PS, however, both these parameters decreased with an increase in FS. The non-collectable loss increased by 0.9% with an increase in FS and DAI; however, PS did not affect the non-collectable losses. The cleaning efficiency increased with an increase in FS, but it decreased with an increase in PS and DAI. The shattering loss increased with an increase in FS and DAI, although PS had no impact. The best combination of operational variables for threshing operation was found for treatment combination of 6 days after last irrigation, with a cylinder peripheral speed of 14.79 m s-1 and a forward speed of 3.0 km h-1. At this combination, threshing efficiency, non-collectable losses, grain breakage, and cleaning efficiency were 99.28%, 4.53%, 2.96%, and 95.89%, respectively. This combine-harvester saved 91.75% in labor costs and 53.71% in harvesting costs compared to the combination of manual harvesting and subsequent threshing using a spike tooth type thresher.

Marker-trait association analysis for easy fruit destemming and mechanical harvestability traits in New Mexican chile pepper (Capsicum annuum L.)

IntroductionChile pepper (Capsicum annuum L.) mechanization is a promising alternative to traditional hand harvesting due to the costs associated with manual harvest, as well as the increasing unavailability of skilled manual chile harvesters. This study aimed to identify single nucleotide polymorphisms (SNPs) associated with mechanical harvestability (MH) and yield-related traits using multi-locus genome-wide association mapping approaches in a C. annuum association mapping population.MethodsA C. annuum association mapping panel for mechanical harvest was manually direct seeded in an augmented block design in two locations. After filtration, imputation, and quality control 27,291 single nucleotide polymorphism (SNP) markers were used for association analyses. Six multi-locus GWAS models were implemented to identify marker trait association.Results and DiscussionMulti-locus GWAS models identified 12 major SNP markers (R2 > 10) across nine chromosomes associated with plant architecture, easy destemming traits, and yield parameters. The presence of a major QTL in chromosome P2, dstem2.1, identified recently to be associated with destemming force, was confirmed. Mature green and mature red yield shared three SNP markers mapped on chromosome P3, P5, and P6 explaining 11.94% to 25.15% of the phenotypic variation. Candidate gene analysis for the significant loci identified 19 candidate genes regulating different phytohormone biosynthesis/signaling, metabolic processes, transcription, methylation, DNA repair/replication, and RNA splicing, with potential roles in controlling plant architecture and morphology. The diverse positions of the associated SNPs suggest the complex nature of these quantitative traits, involvement of multiple genetic factors, and novel significant marker-trait associations. Results from this study will be relevant for genetic improvement of mechanical harvestability traits in New Mexican chile pepper using molecular markerassisted breeding and selection.

S2CPL: A novel method of the harvest evaluation and subsoil 3D cutting-Point location for selective harvesting of green asparagus

Robotic selective harvesting is an ideal method for bionic manual harvest of green asparagus. However, the harvesting robot encounters difficulties in evaluating the suitable harvest due to the tilt and bending of the long stem, as well as determining the precise location of the subsoil cutting-point to prevent damage from bacteria on the cutting surface. This paper proposed the S2CPL model to address the challenges of the harvest evaluation and 3D localization of subsoil cutting-point for selective harvesting of green asparagus in field conditions. Firstly, an RGB-D sensor was used to acquire images and depth information of green asparaguses. Secondly, the improved YOLOv8 by introduced lightweight convolution and attention mechanisms in the feature fusion module to enhance the segmentation accuracy. Thirdly, a 3D morphology extraction method was proposed to calculate the length and diameter of green asparagus by utilizing the image mask fusion with depth information. Finally, harvest evaluation and subsoil 3D cutting-point location were achieved for robotic selective harvesting. In addition, the RGB-D sensor posture was optimized. The test results showed that the Intersection over Union (IoU) of green asparagus segmentation with S2CPL reaches 98.0 %, which outperforms YOLOv5 + uNet, YOLOv7 + uNet and YOLOv8-tiny by 5.60 %, 4.59 % and 1.34 % respectively. The average detection time per image was only 2.0 ms, and the GFLOPS was improved by 23.90 %, 88.49 % and 7.63 % compared with other models. The relative error of the length and diameter were less than 2.98 % and 2.15 %, respectively. The accuracy of location the subsoil cutting-point is more than 99.0 %, and the horizontal positioning error and depth positioning error of cutting-points were less than 6.0 mm and 7.4 mm. The proposed model is of strong robustness even dealing with partial occlusion and motion blur and is suitable with limited computing power to meet the needs of Robotic selective harvesting.

Machine learning system for commercial banana harvesting

The conventional process of visual detection and manual harvesting of the banana bunch has been a known problem faced by the agricultural industry. It is a laborious activity associated with inconsistency in the inspection and grading process, leading to post-harvest losses. Automated fruit harvesting using computer vision empowered by deep learning could significantly impact the visual inspection process domains, allowing consistent harvesting and grading. To achieve the goal of the industry-level harvesting process, this work collects data from professional harvesters from the industry. It investigates six state-of-the-art architectures to find the best solution. 2,685 samples were collected from four different sites with expert opinions from industry harvesters to cut (or harvest) and keep (or not harvest) the banana brunch. Comparative results showed that the DenseNet121 architecture outperformed the other examined architectures, reaching a precision, recall, F1 score, accuracy, and specificity of 85%, 82%, 82%, 83%, and 83%, respectively. In addition, an understanding of the underlying black box nature of the solution was visualized and found adequate. This visual interpretation of the model supports human expert’s criteria for harvesting. This system can assist or replace human experts in the field.

Yield, milling quality, rice quality and preferences of superior and specific location rice varieties

Abstract Grain and rice characteristics are a description of the post-harvest quality of rice which is a consideration for farmers, traders, and consumers in choosing rice to be developed and consumed. This study aims to determine the yield, milling quality, rice quality and preference of 5 rice varieties in Indonesia, they were Inpari 32 HDB, Inpari 42 Agritan GSR, Inpari 45 Dirgahayu, Pamelen and Inpari IR Nutri Zinc. The study used a non-factorial Randomized Group Design, rice varieties as treatments, 5 replications. Rice was harvested using 2 methods, namely sickle (manual harvesting) and combine harvester. Determination of Milling Quality, rough rice was milled using a mini electric grain whitener LTJM-2008 then measured the percentage of hulled rice, unhulled rice, bran, and husk. Determination of rice quality, 25 grams of rice samples were sorted manually and measured the percentage of head rice, broken, groats and damaged then determined the grade of rice based on SNI 6128: 2015. The results showed that the rice yield manually and using a combine harvester was different from the potential yield. The milling quality and rice quality of each variety were significantly different (p=0.05%). Rice quality classification based on SNI 6128:2015 shows that Inpari 42 Agritan GSR and Inpari IR Nutri Zinc was included in Medium I, pamelen and Inpari 32 HDB were included in Medium II, and Inpari 45 Dirgahayu was under Medium III. Inpari 32 HDB was the rice variety with the highest preference.

Fast, Nondestructive and Precise Biomass Measurements Are Possible Using Lidar-Based Convex Hull and Voxelization Algorithms

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

A selective harvesting robot for cherry tomatoes: Design, development, field evaluation analysis

AbstractWith the aging population and increasing labor costs, traditional manual harvesting methods have become less economically efficient. Consequently, research into fully automated harvesting using selective harvesting robots for cherry tomatoes has become a hot topic. However, most of the current research is focused on individual harvesting of large tomatoes, and there is less research on the development of complete systems for harvesting cherry tomatoes in clusters. The purpose of this study is to develop a harvesting robot system capable of picking tomato clusters by cutting their fruit‐bearing pedicels and to evaluate the robot prototype in real greenhouse environments. First, to enhance the grasping stability, a novel end‐effector was designed. This end‐effector utilizes a cam mechanism to achieve asynchronous actions of cutting and grasping with only one power source. Subsequently, a visual perception system was developed to locate the cutting points of the pedicels. This system is divided into two parts: rough positioning of the fruits in the far‐range view and accurate positioning of the cutting points of the pedicels in the close‐range view. Furthermore, it possesses the capability to adaptively infer the approaching pose of the end‐effector based on point cloud features extracted from fruit‐bearing pedicels and stems. Finally, a prototype of the tomato‐harvesting robot was assembled for field trials. The test results demonstrate that in tomato clusters with unobstructed pedicels, the localization success rates for the cutting points were 88.5% and 83.7% in the two greenhouses, respectively, while the harvesting success rates reached 57.7% and 55.4%, respectively. The average cycle time to harvest a tomato cluster was 24 s. The experimental results prove the potential for commercial application of the developed tomato‐harvesting robot and through the analysis of failure cases, discuss directions for future work.

Comparison between Manual Harvesting and Mechanical Harvesting

Comparison between Manual Harvesting and Mechanical Harvesting

The selection of appropriate rootstock and training system towards sustainable production of stone fruits

In stone fruit species, such as peach, cherry, apricot and plum labour requirement is important ranging from 45% to 71% of the total cost of production. In this sense, combining size controlling rootstocks as Rootpac® series, Isthara® and others in peach and plum, or Gisela® and Corette® series in cherry, with intensive bidimensional training systems based on central axis or bi axis resulted in earlier and higher yields, compared with the traditional open vase associated with vigorous rootstocks. The association of these intensive orchards with bidimensional canopies and good accessibility to canopy by labour and machines, is essential to reduce the cost of production. These canopies allow an efficient use of labour and mechanization. Regarding peach, under Ebro Valley (Spain) conditions, the reduction on total cost of production ranged from 15% to 20% and the harvest rate increased by 41%, enhancing also fruit quality due to better light distribution within the tree canopy. In cherry the increase of harvest rate increased by 72% when UFO was compared with the traditional open center. Regarding what in Washington State (US). With European and Japanese varieties, intensification by using Rootpac®20 rootstock and super high density plantings (SHD), allowed to get more than 40 t ha-1 in the third year in Ebro Valley (Spain). With ‘Agen’ European plum, using the same rootstock, under Chilean conditions training trees in a small hedge (SHD) allowed the full mechanization of the harvest by using over-the-row harvester or pedestrian harvest for the needs of fresh market. In this case, manual harvest efficiency increased by 49% compared with the traditional open vase. In all species, the use of size controlling rootstocks offers different options to growers when selecting the training systems concerning skilled labour requirements to train the trees in the first years. Thus, an increase was recorded when the number of axes increased from central axis to bi-, tri- and multi-axes. Furthermore, all these planar canopies were better adapted to the application of precision technologies based on artificial vision (AV) for monitoring pest and diseases, neural network and AV for fruit counting, yield forecasting and robotic harvesting.

Lightweight-Improved YOLOv5s Model for Grape Fruit and Stem Recognition

Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is improved for efficiently identifying grape fruits and stems. On the one hand, it improves the CSP module in YOLOv5s using the Ghost module, reducing model parameters through ghost feature maps and cost-effective linear operations. On the other hand, it replaces traditional convolutions with deep convolutions to further reduce the model’s computational load. The model is trained on datasets under different environments (normal light, low light, strong light, noise) to enhance the model’s generalization and robustness. The model is applied to the recognition of grape fruits and stems, and the experimental results show that the overall accuracy, recall rate, mAP, and F1 score of the model are 96.8%, 97.7%, 98.6%, and 97.2% respectively. The average detection time on a GPU is 4.5 ms, with a frame rate of 221 FPS, and the weight size generated during training is 5.8 MB. Compared to the original YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models under the specific orchard environment of a grape greenhouse, the proposed model improves accuracy by 1%, decreases the recall rate by 0.2%, increases the F1 score by 0.4%, and maintains the same mAP. In terms of weight size, it is reduced by 61.1% compared to the original model, and is only 1.8% and 5.5% of the Faster-RCNN and SSD models, respectively. The FPS is increased by 43.5% compared to the original model, and is 11.05 times and 8.84 times that of the Faster-RCNN and SSD models, respectively. On a CPU, the average detection time is 23.9 ms, with a frame rate of 41.9 FPS, representing a 31% improvement over the original model. The test results demonstrate that the lightweight-improved YOLOv5s model proposed in the study, while maintaining accuracy, significantly reduces the model size, enhances recognition speed, and can provide fast and accurate identification and localization for robotic harvesting.

Drivers of Adoption: Mechanical Harvesting in the Corporate Sector Tea Plantations in Sri Lanka

Tea industry experts emphasize that selective manual harvesting is the best way to maintain leaf quality while preserving tea plant health. However, tea plantations tend to adopt mechanical harvesting to overcome labour shortages and harvesting costs. But the adoption of mechanical harvesting is still low, prompting further investigations. The purpose of this study is to investigate tea plantation managers’ concerns about the factors influencing mechanical harvesting adoption based on a case study as the research strategy. The results demonstrated that the majority of determinants that emerged, as drivers are consistent with the latent constructs, defined in adoption theories. Key drivers are employees’ attitudes, facilitation, continuous monitoring, training of women and skill development through regular practice. The organizational factors that cannot be explained through adoption theories were isolated. The findings concluded that multi-dimensional factors have emerged as the driving force, which will be useful to anyone looking into the real application.

Illuminating the path to sustainable rice harvesting: A solar-based paddy harvester

Paddy is the most important agricultural crop product in Bangladesh since it is widely used in daily life as a main meal intake. Currently, traditional manual harvesting process is widely used to harvest paddy grains in Bangladesh. However, manual harvesting is laborious, time-consuming, and costly. Utilization of modern machineries such as combined paddy harvester machines can increase grain productivity while reducing significantly the number of labour-hours. Combine rice harvesters possess traditional diesel engine that burns gasoline fuels i.e. diesel. The overriding concern of this diesel oil combustion is the production of greenhouse gases that adversely affect the environment. In addition, reports suggest that oil reserve might be depleted to zero in next 40 years. This manuscript presents a comprehensive review on the paddy grain production statistics, the pros and cons of traditional rice crop harvesting processes, and the historical development of mechanization in the agricultural sectors of Bangladesh. It also presents a novel design route to convert commercial diesel-driven combine paddy harvester machine to solar energy-operated combine rice harvester machine with its required accessories. In particular, a new design concept with a remarkable cost estimation process of solar-powered harvesters is proposed. A field study has been conducted in Sonagazi Upazilla under Feni district of Bangladesh to evaluate the potential use of the proposed solar-powered harvester machine. Our study suggests that solar-driven rice harvester is a promising concept in the paddy crop harvesting process and it will reduce the overall costs for short and long-term uses compared to estimated costs of commercial diesel-driven harvesters. To combat the scarcity of gasoline in the future, it is an obvious choice to bring a paradigm shift in solar energy harvesting and its utilization in the agricultural sectors of Bangladesh. It is expected that the proposed solar-driven rice harvester concepts with their in-depth analysis (including costs) would be able to draw attention to business entrepreneurs and investors who will be excited to initiate a program to manufacture solar-powered rice harvester machines in practice.

Planting and Harvesting Mechanization on Productivity, Energy Efficacy and Profitability of Potato Production

Potato occupied the first position among all the vegetables in respect of area and total production in Bangladesh. Effective and efficient use of energy are necessary for an improved agricultural production. Therefore, this research was undertaken for potato production to assess productivity, quantify energy flow and financial profitability of different production systems. The experimented four systems were manual planting and manual harvesting, machine planting and manual harvesting, manual planting and machine harvesting and machine planting and machine harvesting, respectively. The experiment was conducted at Farm Machinery and Postharvest Process (FMP) Engineering Division of Bangladesh Agricultural Research Institute (BARI) during 2017-18. Yield of potato including yield contributing characters for different treatments was not significantly varied. Direct energy was the highest in mechanical planting and harvesting system (25%) and the lower manual planting and harvesting system (18%). Indirect energy shared 82% in manual planting and harvesting system, 78% in machine planting and manual harvesting system, 80% in manual planting and mechanical harvesting system, and 75% in machine planting and machine harvesting system, respectively. The largest source of indirect energy consumption was from fertilizer (22-27% of the total energy). The higher specific energy of 1.93 MJ/kg was found in manually planted and mechanized harvesting system. The higher product value was from manual planting and mechanical harvesting system. The highest BCR (2.25) was found from machine planted and machine harvested followed by manually planted and mechanically harvested plot (2.16), machine planted and manually harvested plot (1.93) and manual planting and harvesting plot (1.91). Thus, mechanically planted and harvested system can be recommended for potato production since it can reduce 52% labour requirement than manual method and can give higher BCR among the tested production methods.

The impact of harvesting method and harvesting month on the quality of Helichrysum italicum (Roth) G. Don. essential oils and oxidative stress

ABSTRACT The chemical properties of Helichrysum italicum (Roth) G.Don. (common name ‘immortelle’), depend largely on the cultivation type, climatic conditions, and cultivation methods. The time and the harvesting method are crucial for enhancing the quality of essential oils. This investigation aimed to evaluate how harvesting month affects the amount and quality of essential oils (EOs). The results showed that the qualitative properties of the essential oil were influenced by the method of harvest (by hand or by machine) and, harvesting time. Harvesting by machine gave better γ-curcumene content, and harvesting by hand gave better a-pinene and neryl acetate quality. It is also observed that Reactive Oxygen Species (ROS) production under machine harvesting raised lipid peroxidation of membranes. In general, better qualitative properties of immortelle essential oil were obtained from manual harvesting.

Dynamics of soil penetration resistance, moisture depletion pattern and crop productivity determined by mechanized cultivation and lifesaving irrigation in zero till blackgram

Rice fallow black gram is grown under the residual moisture situation as a relay crop in heavy texture montmorillonite clay soil under zero till condition. Since the crop is raised during post monsoon season, the crop often experiences terminal stress due to limited water availability and no rainfall. Surface irrigation in montmorillonite clay soil is determent to pulse crop as inundation causes wilting. Therefore, zero tilled rice fallow black gram has to be supplemented with micro irrigation at flowering stage (35 days after sowing) to alleviate moisture stress and to increase the productivity as well. Hence micro farm pond in a corner of one ha field was created to harvest the rain water during monsoon season and the same was utilized to supplement the crop with lifesaving irrigation through mobile sprinkler at flowering stage for the crop grown under conservation agriculture. Soil cracking is also the common phenomena of montmorillonite clay soil where evaporations losses would be more through crack surfaces. The present study was therefore conducted to study the changes in the soil physical properties, crop establishment and productivity in conjunction with mechanized sowing and harvest and supplemental mobile sprinkler irrigation. Sowing of black gram by broadcasting 10 days prior to the manual harvest of rice, manual drawn single row seed drill after the machine harvest of rice and sowing by broadcasting at 4 days prior to machine harvest of rice was experimented separately and in combination with lifesaving irrigation. Results indicated that the number of wheel passes and lifesaving irrigation had a very strong impact on soil penetration resistance and soil moisture. Combined harvester followed by no till seed drill increased the soil penetration resistance in all the layers (0–5 cm, 5–10 cm and 10–15 cm). Two passes of wheel increased the mean soil penetration resistance from 407 KPa to 502 KPa. The soil penetration resistance (0–5 cm) at harvest shown that black gram sown by manual broadcasting 10 days prior to manual harvest of paddy supplemented with life irrigation on 30 DAS reduced the soil penetration resistance from 690 Kpa to 500 Kpa, 740 Kpa to 600 Kpa and 760 Kpa to 620 Kpa respectively at 0–5 cm, 5–10 cm and 10–15 cm layer. In general, moisture depletion rate was rapid in the surface layer of 0–5 cm as compared to other layers of 5–10 cm and 10–15 cm up to 30 DAS (Flowering stage). The moisture content and the soil penetration resistance had an inverse relationship. The soil penetration resistance also had an inverse relationship with the root length in which the root length lowers as the soil penetration resistance increases. The soil crack measured at 60 DAS was deeper with no till seed drill (width of 3.94 cm and depth of 13.67 cm) which was mainly due to surface layer compaction. The relative water content, specific leaf weight and chlorophyll content were significantly improved through the supplemental irrigation given on 30 DAS irrespective of crop establishment methods. The results further indicated that compaction of ploughed layer in the moist soil due to combined harvester and no till seed drill had a negative impact on yield (457 kg ha−1), which was improved by 19.03 per cent due to increased soil moisture with supplemental irrigation. The mean yield increase across different treatments due to supplemental lifesaving irrigation through mobile sprinkler was 20.4 per cent.

Influence of Mechanization on Yield and Economics of Soybean

The present study was conducted at farmers fields of Adilabad district, Telangana state, India during kharif seasons of 2019 to 2021 (for 3 seasons) to appraise the effect of mechanization on soybean yield as well as economics. In this study, sowing by tractor operated seed cum fertilizer drill and harvesting by combined harvester was compared with the farmer’s practice (Bullock drawn seed drill sowing and manual harvesting and threshing). In comparison to the conventional method, the sowing of soybean with tractor operated seed cum fertilizer drill and harvesting with combined harvester found to be superior in seed yield and average soybean productivity with mechanization was 1870 kg ha-1, with a yield increase of 2.20% over conventional method of sowing (1830 kg ha-1). Similarly, the economics of soybean were found to be highest with mechanization in terms of gross returns (Rs 79064 ha-1) and net returns (Rs 52464 ha-1) with a B:C ratio of 2.97:1 compared to conventional method which generated the gross returns of Rs. 77150 ha-1 and net returns of Rs 46966 ha-1 with a B:C ratio of 2.56:1.